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Cancer multicellular spheroids: Volume assessment from a single 2D projection
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journal homepage: www.intl.elsevierhealth.com/journals/cmpb
Cancer multicellular spheroids: Volume assessment from a single 2D projection Filippo Piccinini a , Anna Tesei b , Chiara Arienti b , Alessandro Bevilacqua a,c,∗ a
Advanced Research Center on Electronic Systems (ARCES) for Information and Communication Technologies “E. De Castro”, University of Bologna, Italy b Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) s.r.l., IRCCS, Biosciences Laboratory, Meldola, FC, Italy c Department of Computer Science and Engineering (DISI), University of Bologna, Italy
a r t i c l e
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a b s t r a c t
Article history:
Volume is one of the most important features for the characterization of a tumour on a
Received 24 July 2014
macroscopic scale. It is often used to assess the effectiveness of care treatments, thus mak-
Received in revised form
ing its correct evaluation a crucial issue for patient care. Similarly, volume is a key feature on
3 December 2014
a microscopic scale. Multicellular cancer spheroids are 3D tumour models widely employed
Accepted 15 December 2014
in pre-clinical studies to test the effects of drugs and radiotherapy treatments. Very few methods have been proposed to estimate the tumour volume arising from a 2D projection
Keywords:
of multicellular spheroids, and even fewer have been designed to provide a 3D reconstruc-
Pre-clinical oncology
tion of the tumour shape. In this work, we propose Reconstruction and Visualization from a
Widefield microscopy
Single Projection (ReViSP), an automatic method conceived to reconstruct the 3D surface and
Multi-cellular tumour aggregates
estimate the volume of single cancer multicellular spheroids, or even of spheroid cultures.
Volume rendering
As the input parameter ReViSP requires only one 2D projection, which could be a widefield
Software application
microscope image. We assessed the effectiveness of our method by comparing it with other
Image processing
approaches. To this purpose, we used a new strategy that allowed us to achieve accurate volume measurements based on the analysis of home-made 3D objects, built by mimicking the spheroid morphology. The results confirmed the effectiveness of our method for both 3D reconstruction and volume assessment. ReViSP software is distributed as an open source tool. © 2014 Elsevier Ireland Ltd. All rights reserved.
1.
Introduction
Shape and volume are among the most relevant features for the characterization of the appearance of a tumour. A
correct shape assessment is at the basis of surgical planning [1], while the correct volume definition is essential to assess the effectiveness of cancer treatments and therapies and serves in the subsequent decision making process [2].
∗ Corresponding author at: Prof. Alessandro Bevilacqua, University of Bologna, Via Toffano 2/2, I-40125 Bologna, Italy. Tel.: +39 0512095409; fax: +39 0512095410. E-mail addresses: [email protected] (F. Piccinini), [email protected] (A. Tesei), [email protected] (C. Arienti), [email protected] (A. Bevilacqua).
http://dx.doi.org/10.1016/j.cmpb.2014.12.003 0169-2607/© 2014 Elsevier Ireland Ltd. All rights reserved.
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In particular, it has been shown that tumour volume is an important predictor of the clinical outcome of patients with different cancers [3]; it has also been used as a parameter to decide whether or not to exclude patients from curative chemotherapy/radiotherapy protocols [4]. Thus, the accurate estimate of tumour volume becomes fundamental for patient care. This importance of volume also holds at microscopic level. Among the different cancer models, cancer spheroids are particularly appealing, hence their growing popularity [5,6]. They are multicellular aggregates of “large” dimensions (i.e., up to 1 mm in diameter [7]) obtained in vitro from single cells from different cell lines [8]. Spheroids span the gap between monolayer cell cultures and whole-animal systems [9] and are used as a solid 3D model in a wide range of applications [10], e.g. to test drug dosages [11], radiotherapy treatments [12], and, more generally, to define new protocols for different tumour types [13]. Several methods and systems are employed to build spheroids [14]. Standard plastic well plates are typically used to maintain spheroids and analyze morphological and biological changes [15], then correlated to the effect of different drug dosages and radiotherapy treatments [16], also in high-throughput screens [17]. Tumour volume assessment is needed to obtain reliable data [18]. Nevertheless, due to the lack of accurate methods to estimate volume, the decrease/increase in the area of the maximum cross-section of spheroids is typically used to evaluate the effectiveness of treatments being evaluated [19]. However, this strategy may lead to several errors that can compromise the entire analysis because spheroids with same projection area can have very different volumes and vice versa (Fig. 1). In this work, we propose the Reconstruction and Visualization from a Single Projection (ReViSP) tool, a 3D volume rendering method conceived to reconstruct the 3D shape of spheroids, in addition to estimating volume by counting the voxels (i.e., volume elements) fully included on the 3D surface. It requires only one projection as the input parameter, which can be an image acquired with a widefield microscope, and automatically shows a 3D rendering of the spheroids. It is worth noting that the widefield microscope only provides a visualization from the top (or the bottom, in the case of an inverted microscope) which results in a single projection of a cross-section of the spheroid. The reconstruction of the whole 3D volume from a single projection is an extremely challenging task. In particular, the problem of reconstructing volumes from 2D projections arises in a large number of scientific fields, including computerized tomography, electron microscopy, X-ray microscopy, radiology, radio astronomy and holography [20]. Several reconstruction methods have been proposed since the beginning of the past century, but all require one sequence of cross-sections [21–23], or even a stereo image which provides depth information [24]. Nevertheless, a number of assumptions can help to estimate spheroid volume even from a widefield image only. First, due to gravity, spheroids lie on the bottom of the plate, typically in a stable equilibrium position. Accordingly, it is reasonable to assume that the image acquired depicts the widest cross-section of the spheroid. In addition, the similarity of the area values computed from as many images acquired after that a spheroid has reached its equilibrium position (after
being artificially moved away), suggests a spherical symmetry [25] of the spheroid with respect to the principal axis. This is also the reason why a sphere [18] or an ellipsoid [26] is often deemed a valid model to estimate spheroid volume. Starting from these considerations, we designed our method to reconstruct the 3D surface of spheroids by analysing the perimeter of the cross-section, computing the local distance between points perpendicular to the principal axis, and assuming a local circular symmetry in the z-dimension. We also devised a specific control to separately consider and reconstruct protuberances and protrusions, frequent in the spheroids of several cell lines (see, for instance, human lung cancer spheroids in Fig. 2, human epithelial carcinoma reported in Fig. 4 of [17], and rat hepatocytes reported in Fig. 1d of [8]). ReViSP is written in MATLAB (©, The MathWorks, Inc., MA, USA). Both the source code and a standalone executable version (i.e., not requiring MATLAB being installed) are freely available at: http://sourceforge.net/p/revisp, together with sample images that can be downloaded as well. To assess the effectiveness of our implementation, we propose a new comparative approach based on the analysis of home-made 3D objects built to mimic spheroid morphology. Starting from the microscopic analysis of several spheroids at the confocal microscope (Fig. 3), we built some representative macroscopic 3D models (in practice, synthetic spheroids) and then took shots of them by using a professional high-quality digital camera. Finally, we compared the volume values estimated using ReViSP with the real volume (technically, the ground truth), computed using water, a graduated cylinder and a precision balance. The results obtained confirmed the effectiveness of ReViSP in estimating the spheroid volume whilst also permitting a qualitative assessment of the reconstructed tumour shape. A very early version of this work, capable to work with single objects with fairly regular shapes, was proposed in [27]. In the present work, the reconstruction method was extended to include the class of spheroids with protuberances, this yielding a wider generality and an improved robustness of the method. Above all, the reconstruction algorithm was revised to work even when several spheroids are present in one image. Most important, this would also permit to deal with a culture of spheroids, acquired by building a mosaic of images [28]. In order to have a wide overview of spheroid morphology phenotypes, three different systems were employed to generate cancer multicellular spheroids. Then, new synthetic spheroids were employed in several experiments carried out to assess the proposed method’s performance in estimating the volume of spheroids, also with protuberances. ReViSP is distributed as an open source software tool endowed with a Graphical User Interface (GUI). In particular, it is provided as supplementary material by setting up a specific webpage also to host sample sets and future releases. The next sections are organized as follows. Section 2 presents a short overview of methods used to estimate biovolumes. Section 3 describes the materials used in the experiments. Section 4 provides a detailed description of the proposed method, the results of which are presented and discussed in Section 5. Finally, Section 6 reports the main findings of the work.
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Fig. 1 – Synthetic spheroids and ellipsoids of same volume. The solids in (a), (b) and (c) are spherically symmetric around the principal axis and lie on the x–y plane in a stable equilibrium position. Although they all have an identical volume, their projections (i.e., their cross-sections) have areas of different sizes. In particular, (d) is 3/4 of (e) and 2/3 of (f).
Methods for volume reconstruction from 2. a single projection Very few methods have been proposed in the literature to estimate tumour volume (starting) from a single projection image [29]. In particular, the first researchers to tackle this problem
were Gaylord and Clowes in 1906 [30] who used the formula of the ellipsoid (Eq. (1)) to approximate tumour volume starting from the computation of the maximum diameter M, passing through the centre of mass and its orthogonal diameter P:
VELLIPSOID =
· M · P2 . 6
(1)
Fig. 2 – Multicellular cancer spheroids obtained from lung cancer cells (line A549), built using different commercial systems (a–c), and characterized by a different number of protuberances (d–f). In particular, the spheroid in (a) was built using a RCCS-8DQ bioreactor (Synthecon, Inc.), in (b) with a Perfecta3D hanging-drop plate (3DBiomatrix, Inc.) and in (c) with a GravityPLUSTM hanging-drop plate (InSphero, Schlieren, Switzerland). The spheroids in (a–c) have a single small protuberance, those in (d) and (e) one and two large spherical protuberances, respectively, and the spheroid in (d) has several irregular-shaped protuberances. The scale bar in the lower-left corner of the images represents 250 m.
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Fig. 3 – Microscopic morphology of common spheroids. Confocal images were exported using Nikon NIS Elements AR software. (a) Spherical spheroid, (b) elliptical spheroid, (c) spheroid with irregular shape and one protuberance at the bottom right side. As can be seen in all the images, the maximum depth reached with the confocal microscope corresponds to about half the diameter of each spheroid. Scale bars represent 200 m.
Woglom [31], in contrast, proposed to use the formula of the sphere (Eq. (2)) by simply computing the average diameter D: VSPHERE =
· D3 . 6
(2)
Over time, several other formulas were proposed to estimate tumour volume, still supposing a sphere or an ellipsoid model. Tomayko and Reynolds, in the review work of 1989, compared 19 different formulas to estimate the tumour volume [32]. However, none of them seems to provide an accurate approach to obtain reliable data. In fact, the spheroids (and, in general, the tumours [33]) are typically characterized by a complex 3D morphology and there are only a few cases in which a sphere or an ellipsoid model can hold. Furthermore, even when the volume is estimated with a good approximation, these models provide a very unrealistic 3D representation that is not useful for clinical evaluations. No revolutionary breakthrough methods have been proposed since Sieracki et al., again in 1989, described an image processing method based on the object revolution around its maximum (straight) axis [34]. In practice, the authors analyzed the object perimeter by computing the local distance between points perpendicular to the maximum axis, as we do, a strategy that was better than computing single diameters and using a specific formula [35]. Although the method was designed to estimate the volume of bacterial cells, the authors only exploited the assumption of local spherical symmetry, which can also hold for multicellular spheroids. Along the same lines, Zeder et al. in 2011 proposed a new algorithm to estimate 3D volume based on the segmentation of the 2D object perimeter into triangles, subsequently approximated as 3D “half-cylinders” [36]. Although the algorithm was fairly complex, the authors provided a very user-friendly implementation. On the basis of the experiments carried out by the authors using synthetic bacteria, the method seemed to provide a good volume approximation, which however cannot be assessed visually, since the representation of the 3D surface of the object was not provided. Finally, in case the method
is used to estimate the volume of a multicellular spheroid, it must be pointed out that spheroids with more than one protuberance were not taken into consideration, thus potentially compromising the volume estimation. Above all, it is worth noting that the accuracy has not been validated with real-world objects. In conclusion, to our knowledge, ReViSP is the first software tool explicitly conceived to estimate the volume of single, or a culture of, cancer spheroids that also takes into consideration protuberances and provides a GUI to view and interact (i.e., to rotate, to scale, etc.) with the 3D reconstruction of the spheroid surface.
3.
Materials
3.1.
Cancer multicellular spheroids: creation
To analyze different spheroid phenotypes, we prepared several 96-well plates containing spheroids created using A549 cells, a commercial cell line derived from a primary lung cancer (American Type Culture Collection, ATCC, Rockville, MD, USA) commonly used to build spheroids [37]. Cells were grown in F12K medium (ATCC) supplemented with 10% fetal bovine serum (FBS, EuroClone, Milan, Italy), maintained as a monolayer in a 5% CO2 humidified atmosphere at 37 ◦ C, subcultured weekly using trypsin-EDTA solution (EuroClone) and checked periodically for mycoplasma contamination by MycoAlertTM Mycoplasma Detection Kit (Lonza, Basel, Switzerland). Finally, we used three different commercial systems to grow the multicellular spheroids: an RCCS-8DQ bioreactor [38] (Synthecon, Inc., Houston, TX, USA), and Perfecta3D [39] (3DBiomatrix, Inc., Ann Arbor, MI, USA) and GravityPLUSTM hanging-drop plates [40] (InSphero, Schlieren, Switzerland). A detailed description about cell preparation and the protocol used to create the spheroids with the bioreactor can be found in [10]. For the Perfecta3D and GravityPLUS hanging-drop plates, we followed the standard protocol provided by the manufacturers. The final size of the spheroids obtained with the bioreactor was between 500 m and 1 mm, while for the spheroids built with
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Fig. 4 – Different spheroid phenotypes. (a–d) Home-made 3D models of spheroids built to mimic the morphology of real multicellular cancer spheroids. (e–p) Spheroids built using an RCCS-8DQ bioreactor and (q–t) a Perfecta3D hanging-drop plate. (u–x) Spheroids built using a GravityPLUSTM hanging-drop plate and then maintained in culture by using a multi-well plate with conical-shaped wells (GravityTRAPTM , InSphero). In addition to the spherical (e), (i), (m), (q), (u) and ellipsoidal shapes (f), (j), (n), (r), (v), it is common to find also spheroids with “8”-like shape (g), (k), (o), (s), (w) or irregular shape (k), (l), (p), (t), (x).
the hanging-drop plates the size was between 250 m and 500 m. Although we expected to obtaining different spheroid phenotypes [13,41], the very high number of aggregates with irregular shapes was quite surprising (Fig. 4).
3.2.
Cancer multicellular spheroids: image acquisition
We imaged all the cancer spheroids using an inverted Olympus IX51 microscope equipped with a Nikon Digital Sight DS-Vi1 camera (CCD vision sensor, square pixels of 4.4 m side
length, 1600 × 1200 pixel resolution, 3-channel images, 8-bit grey level). Images were acquired in brightfield using an Olympus UPlanFl 4×/0.13na as a standard objective lens (Fig. 4). Then, to better analyze the different spheroid phenotypes, we acquired z-stack images (1024 × 1024 pixel resolution, 3-channel images, 12-bit grey level) representing several spheroids using a Nikon Eclipse Ti microscope equipped with an A1R confocal laser scanner (Nikon), temperature and CO2 controllers (Okolab, Ottaviano, Naples, Italy) and a Nikon Plan Fluor 10×/0.30na Ph1 DLL standard objective lens. In
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Fig. 5 – Home-made synthetic 3D spheroids. From left to right: Spheroid with one (G2A ), two (G2B ) and more protuberances (G2C ). The white scale bar represents 10 mm.
particular, the spheroids analyzed with the confocal microscope were stained using the LIVE/DEAD cell assay kit L-3224 (Molecular Probes, Inc., Leiden, The Netherlands) which is based on the simultaneous determination of live and dead cells with two fluorescent probes. Live cells are stained green by calcein AM due to their esterase activity, while the nuclei of dead cells are stained red by ethidium homodimer 1 (Fig. 3).
3.3.
Synthetic spheroids: creation
The real volume of a spheroid volume must be known in order to assess the accuracy of a volume estimating method. However, the volume of a cancer spheroid with a 1-mm maximum diameter is not easy to measure with a standard equipment, and synthetic objects designed by combining regular shapes through computer-aided drafting (CAD) software are typically used to overcome this problem [36]. Nevertheless, a “composite regular-shaped” object is not really capable of mimicking a spheroid characterized by several irregularities with respect to spheres and ellipsoids. Thus, starting from the observation of the morphology of the microscopic bio-spheroids, we proposed a validation approach based on synthetic 3D objects. In particular, we manually constructed some macroscopic 3D models (approximate scale 1:30) with morphologies very similar to those of cancer spheroids (Fig. 4a–d). To this purpose, we built up a first group (G1) of four representative synthetic 3D spheroids: one with a spherical shape (hereafter G1A , Fig. 4a), a second mimicking an ellipse/ovoid (G1B , Fig. 4b), a third with a “8”-like 3D shape (achieved by squeezing the central part of an ellipse, G1C , Fig. 4c), and a fourth of irregular shape (G1D , Fig. 4d). We then assessed the performance of all the reconstruction methods considered by building a second group (G2) of synthetic spheroids by simply stitching spheres of different dimensions to the main one to mimic spheroids with one (G2A , Fig. 5a), two (G2B , Fig. 5b) or more protuberances (G2C , Fig. 5c). All the synthetic spheroids were built using a commercial product called DAS (distributed by FILA, Pero, Milan, Italy), a malleable mineral-based polymer clay for modelling. Although we tried to make the objects spherically symmetric around the principal axis, being home-made they all had some degree of imperfection; this, however, helped to mimic real cancer multicellular spheroids. Finally, we indirectly
computed the real volume of the 3D models, first by experimentally estimating the density of the DAS using a graduated cylinder and precision balance (Denver Pinnacle PI-114, Denver Instrument, Bohemia, NY, USA) and then by weighing them all. This procedure also offers the possibility to perform repeated measures of the real volume of 3D objects, with a great accuracy, this being fundamental to allow a fair comparison between the different methods tested and subsequently ranking based on their estimated volume.
3.4.
Synthetic spheroids: image acquisition
We compared the different methods by placing the DAS objects in a stable equilibrium position on a flat table and acquiring an image for each object with a manual high-quality, professional Nikon D800 reflex camera (CMOS vision sensor, square pixels of 4.7 m side length, 5520 × 3680 pixel resolution, 3-channel, 8-bit images) fixed perpendicularly to the table through a dedicated tripod. A telecentric commercial Nikon AF-S VR Micro-Nikkor 105 mm f/2.8G was used as an objective lens. The spatial resolution factor of the camera was experimentally calculated by keeping the set-up of the acquisition system unchanged and acquiring images using several squared test-pieces of known dimension.
3.5.
Revisp software tool
ReViSP is freely distributed as an open source software tool. In particular, the 32-bit and 64-bit standalone executable versions of ReViSP and the source code can be found at http://sourceforge.net/p/revisp. Also, all the images used in the experiments are available on demand. ReViSP is endowed with a simple GUI (Fig. 6) that makes it particularly userfriendly. Nevertheless, it is worth noting that the executable versions can be also executed as a console application, via command line. Consequently, it can be employed in all programmes that include scripting facilities. The only input parameters required are the destination folder where the binary masks of the spheroids to be reconstructed are stored, and the name of the images to be analyzed. It is also possible to define the conversion coefficient m/pixels to automatically obtain the volume in cubic micrometres (provided in voxels
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masks handled through the “ROI manager” plug-in of ImageJ [42], an open source image processing software widely used in microscopy for different purposes [43]. Notably, to obtain fine 3D rendering as a final result, it is important to start from the mask relating to the widest cross-section of the spheroid. To this purpose, several images are typically acquired by scanning the object along the z-axis, and the final fully focused 2D image is reconstructed by merging the content of the different images [44] previously corrected for radial fall-off attenuation of the image intensity [45]. Once the binary mask of the spheroid has been obtained, it is automatically divided into sections comprising the main core of the spheroid and any protuberances that are present (specific details are provided in Section 4.3), after which all the single objects are separately reconstructed and merged to obtain fine 3D rendering. In case of spheroids with a necrotic core, we suggest providing as the input of ReViSP the entire mask of the spheroid and the mask referring to the necrotic core, and then compute the “effective” volume as their difference.
4.2. Fig. 6 – A snapshot of the ReViSP GUI main window. Each button is coupled with a help menu, thus rendering the ReViSP particularly user-friendly.
by default). Although the algorithm has not been optimized, the time required to reconstruct one spheroid is compliant with online processing. We performed the experiments using an entry-level PC (Intel Core i5, CPU 2.27 GHz, 4 GB RAM). In particular, we assessed the computational performance by measuring the elapsed time needed to analyze a sequence of 100 very large binary masks (5520 × 3680 pixels each) stored on the hard disk. The total time required was about 30 min, with an average of 18.6 s per mask. Finally, in addition to the ReViSP source code, the MATLAB functions we devised to compute the volume according to Eqs. (1) and (2) (referring to the sphere and the ellipsoid model, respectively) are also provided as supplemental material (http://sourceforge.net/p/revisp) for the sake of completeness.
4.
Methods
Fig. 7 outlines the approach conceived to reconstruct and visualize the 3D surface of the spheroids whilst estimating their volume. In particular, ReViSP, like all the other methods compared in the experiments, only requires the binary mask (black and white image with 0 for the background and 1 for the foreground) of the spheroid to be analyzed as the input parameter. The 3D surface of each spheroid is then reconstructed by analysing the spheroid perimeter, subdividing main core and protuberances as different objects. The volume consists of the number of voxels contained in the whole 3D surface, provided in output. More details on the different steps of the proposed methods are provided in the next subsections.
4.1.
Image pre-processing
Starting from the RGB images acquired, we manually segmented the synthetic spheroids to obtain the black and white
Image rendering
All the single objects (meant as parts) of the binary mask are separately processed one at a time according to the following steps: 1. detection of the maximum straight diameter passing through the centre of mass of the object (red dashed line in Fig. 8a); 2. rotation of the object to have the maximum diameter parallel to the y-axis of the image and for each y-position, calculation of the x-length of all the perpendicular segments; 3. creation of an x-y map (called 3D map) in which the local height (in pixels) of the 3D surface is reported in relation to the x-y plane passing through the centre of mass of the spheroid and parallel to the image plane (Fig. 8b). The map is built by fitting, for each y-position, a 3D circle with diameter equal to the length of the local x-segment; 4. 3D surface rendering of the object (Fig. 8c) by considering the x-y values of the 3D map as the height (in pixels) of both halves of the spheroid where the top and the bottom parts are considered perfectly symmetric with respect to the x-y plane passing through the centre of mass of the spheroid and parallel to the image plane.
4.3.
Analysis of protuberances
Several brightfield images of cancer spheroids with protuberances are shown in Fig. 2. We devised a strategy aimed at separating the main spheroid from the protuberances to obtain fine 3D reconstruction. It is important to remember that these “appendices”, if not adequately analyzed, may compromise the entire reconstruction process (Fig. 9). Starting from the concavity points of the spheroid perimeter, we used the watershed transform [46] to detect and segment the protuberances. Once the labelled binary mask subdividing protuberances and main core of the spheroid had been obtained (Fig. 9c), we considered every single object (meant as single parts of the binary mask) following the four steps
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Fig. 7 – ReViSP flow-chart. ReViSP requires in input the binary mask of the spheroids to be analyzed. The 3D surface of each spheroid is then reconstructed by separately analysing main core and single protuberances. Finally, the volume is estimated by counting the voxels encompassed by the whole 3D surface.
reported in Section 4.2. At the end, to estimate the final volume (in our approach the final volume estimated does not simply correspond to the sum of the volume of the single parts) and provide a realistic connection between the different parts of the spheroid, we connected the single 3D parts reconstructed using cylindrical connections whose base has as the same diameter as the length of the contact line between the main core of the spheroid and the single protuberance, and the height locally adapted to follow the curvatures of the two objects to be connected. Fig. 9e and f shows as one of the two protuberances of the object reported in Fig. 9d appears before (Fig. 9e) and after (Fig. 9f) being connected to the main core of the object through the cylindrical connection.
4.4.
the culture of spheroids is automatically obtained by summing the volume of each single spheroid. In particular, when using the ReViSP GUI, the volume of both single spheroids and the whole culture are reported through a pop-up window.
4.5.
Starting from the binary mask obtained for each DAS model reported in Figs. 4a–d and 5, we computed the volume with ReViSP and with four other methods, hereafter named as ELLIPSOID (Eq. (1)), SPHERE (Eq. (2)), SIERACKI [34] and ZEDER [36]. Finally, we determined the absolute percentage errors (E%) between the ground truth (GT) volume and the values estimated (V) with the different methods, according to Eq. (3) [34]:
Analysis of a spheroid culture E% =
ReViSP was conceived to permit the analysis of a culture of spheroids, i.e. a group of spheroids imaged together. Whether the spheroids are contained in a single flask or a well of a plate, an automatic mosaicing software tool, e.g. Autostitch [47] or MicroMos [48], can be used to acquire a composite widearea image (namely, a mosaic) that is representative of the whole sample. Once the mosaic of a whole culture has been obtained (Fig. 10a), the single spheroids must be segmented as reported in Section 4.1. No additional prior information is required. The single 3D surfaces of the spheroids are automatically reconstructed one at a time by following the steps reported in Section 4.3, and 3D rendering of the whole culture is shown as the output (Fig. 10b). Finally, the total volume of
Comparison approach
5.
|V − GT| · 100. GT
(3)
Results and discussion
Our experiments aimed to compare the quality of ReViSP with other approaches used to estimate the volume of an object starting from a single projection image. The 3D reconstructions obtained with ReViSP for the objects described in Section 3.3 and illustrated in Figs. 4a–d are shown in Fig. 11a–d. Table 1 reports the E values computed with the different methods according to Eq. (3). The best results are highlighted in bold. With regard to G1A , all the methods yielded an error lower than 2%, indicating that all the methods tested accurately
Fig. 8 – Method proposed to reconstruct the 3D surface starting from the binary mask of the object. First, the direction of the y-axis of the image plane is assigned to match the direction of the maximum axis of the object (a). The x-y 3D map is then computed by fitting a 3D circle for each y-position (b). Finally, the 3D surface is reconstructed (c) considering the x–y values of the 3D map as height in pixels of half of the object (symmetric with respect to the x–y plane passing through the centre of mass of the spheroid).
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Fig. 9 – Separate analysis of the protuberances is fundamental to obtain reliable 3D reconstruction and volume assessment. (a) Binary mask of a spheroid with two spherical protuberances. The principal axis is shown in red. (b) 3D reconstruction obtained without separately analysing the protuberances. The 3D surface was obtained by simply assuming a local spherical symmetry around the principal axis. (c) Labelled binary mask obtained using the approach we proposed to detect and segment the protuberances. (d) 3D surface obtained by processing (a) without physically connecting the single parts (e). (f) A detail related to the more realistic 3D reconstruction attained by using cylindrical connections to “stitch” protuberances to the main core of the object.
estimated the volume of a spheroid when its shape was close to a real sphere. Conversely, for all the remaining 3D models of G1, ReViSP, SIERACKI and ZEDER yielded similar results (maximum percentage error E% always lower than 8%) and always performed better than SPHERE and ELLIPSOID. In particular, SPHERE had an error rate lower than 10% for G1D , but very high
E values for both G1B and G1C . Similar results were obtained for ELLIPSOID which, however, also showed the worst result (33% error for G1C ). In analysing the average E% (Table 1, last row), it can be seen that ReViSP was generally the best model and that the performance of SIERACKI and ZEDER was comparable with ours (difference in average E% lower than 2%). This was
Fig. 10 – Culture of spheroids contained in a single well. (a) Mosaic of a whole well of a 96-well plate. The mosaic was obtained first by manually acquiring 40 overlapping images to cover the entire well, then using MicroMos, which is capable of merging a sequence of previously acquired overlapping images into a high accurate mosaic stitch. Once the mosaic was obtained, it was manually segmented to be processed by ReViSP. The visualization of the 3D surface of the spheroid culture is shown in (b).
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Fig. 11 – 3D surfaces relating to (a) G1A (Fig. 4a), (b) G1B (Fig. 4b), (c) G1C (Fig. 4c), (d) G1D (Fig. 4d), (e) G2A (Fig. 5a), (f) G2B (Fig. 5b), and (g) G2C (Fig. 5c). The surfaces were reconstructed using ReViSP.
Table 1 – E% values obtained by the different methods using the G1 synthetic spheroids. SPHERE G1A G1B G1C G1D Average E% G1
0.69 23.97 21.91 9.00 13.89
ELLIPSOID
SIERACKI
1.99 8.15 33.12 16.10 14.84
1.09 2.45 4.34 3.79 2.92
ZEDER
ReViSP
0.74 2.01 4.96 7.34 3.76
1.26 2.08 1.65 3.55 2.14
ZEDER
ReViSP
7.16 9.35 10.66 9.06
1.02 5.89 6.55 4.49
For each row the best value (lower E%) is reported in bold.
Table 2 – E% values obtained by the different methods using the G2 synthetic spheroids. SPHERE G2A G2B G2C Average E% G2
17.89 11.79 19.23 16.30
ELLIPSOID
SIERACKI
23.03 6.45 162.96 64.15
5.28 13.77 37.88 18.98
For each row the best value (lower E%) is reported in bold.
further confirmed by analysing the specific cases: ReViSP results were either the best (for G1C and G1D ) or differed at most 1% from those of the best method in each case. Similarly, the performance of SIERACKI and ZEDER never differed more than 5% from that of the best method. Overall, ZEDER, SIERACKI and ReViSP can be considered comparable methods, with an average E% lower than 5%, much lower than the average errors achieved by SPHERE and ELLIPSOID (higher than 10%). The scenario changed when we considered the synthetic 3D models of G2 that mimic spheroids with protuberances. Fig. 11e–g shows the 3D reconstructions obtained with ReViSP for the objects shown in Fig. 5, while Table 2 reports the E% values computed for the different methods. ReViSP was always the best method. Moreover, it was the only method that consistently achieved results lower than 8%, in specific cases and on average. Considering the latter (Table 2, last row), it is worthy of note that SIERACKI and ZEDER were no longer comparable with ReViSP, and that only ZEDER
was clearly superior to SPHERE and ELLIPSOID. In particular, ELLIPSOID showed a very poor performance (E% higher than 162% for G2C ), indicating that the estimation of perpendicular diameters in spheroids with protuberances can completely misdirect volume estimation.
6.
Conclusions
In this work, we introduced ReViSP, a user-friendly software tool conceived to reconstruct and visualize the 3D surface of a culture of cancer spheroids by using single images or mosaics acquired with a widefield microscope. The only prior information required is the binary mask of the objects to be segmented and the 3D reconstruction exploits only the morphological appearance of the spheroids. Once 3D rendering of the object has been obtained, the volume can be automatically estimated by counting the voxels inside the surface.
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As a matter of fact, the approach we proposed could also work for a number of in vivo tumours, although the assumptions regarding local sphericity and stable equilibrium make our approach suitable mainly for multicellular aggregates. To compare the accuracy of ReViSP with previously proposed volume-estimating methods, we used a new approach based on home-made 3D models built by mimicking the morphology of cancer multicellular spheroids. The results obtained by ReViSP were very promising and superior to those of the other methods tested (average error lower than 5%). It is worthy of note that the 3D rendering of the objects analyzed can be visualized with ReViSP, thus permitting a visual assessment of the outcome. However, the most important finding to emerge from our study was the fact that some commonly used methods to estimate the volume of a spheroid are usually characterized by a very high average error (higher than 10%) which could completely mislead drug-testing analyses, especially if the cell lines used yield spheroids with protuberances. In conclusion, meanwhile sensitizing the reader about this problem, we provide an effective solution to computing the volume of cancer spheroid cultures by exploiting a single 2D projection. Our results confirm that the approach may be suggested as the most likely candidate for the new gold standard method to estimate spheroid volume when only a single 2D projection is available. The standalone executable versions of ReViSP are freely available at: http://sourceforge.net/p/revisp, as well as the source code. Since ReViSP can be also executed as a console application, this allows it to be included in batch files or scripts so to be easily employed together with the software of automatic plate readers endowed with a digital camera (e.g., ImageXpress® Micro Widefield High Content Imaging System, Molecular Devices, Silicon Valley, CA, USA; Dainippon SCREEN Cell3iMager, InSphero).
Conflict of interest The authors declare that they have no conflict of interest.
[2]
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[6]
[7]
[8]
[9]
[10]
[11]
Acknowledgements We thank FILA (Pero, Milan, Italy) for providing the information on the DAS material; 3D Biomatrix Inc. (Ann Arbor, Michigan, USA) and InSphero (Schlieren, Switzerland) for supplying the hanging-drop plates to build the spheroids; Alessandro Gherardi (Computer Vision Group, University of Bologna) and Nicola Garavini (Faenza, Ravenna, Italy) for image acquisition support; Enrico Lucarelli and Serena Duchi (Osteoarticular Regeneration Laboratory, Rizzoli Orthopaedic Institute, Bologna) for analysing the 3D shapes of the spheroids using a confocal microscope; Gráinne Tierney (IRST, Meldola, Italy) for editorial assistance and English revision of the manuscript.
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journal homepage: www.intl.elsevierhealth.com/journals/cmpb
Cancer multicellular spheroids: Volume assessment from a single 2D projection Filippo Piccinini a , Anna Tesei b , Chiara Arienti b , Alessandro Bevilacqua a,c,∗ a
Advanced Research Center on Electronic Systems (ARCES) for Information and Communication Technologies “E. De Castro”, University of Bologna, Italy b Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) s.r.l., IRCCS, Biosciences Laboratory, Meldola, FC, Italy c Department of Computer Science and Engineering (DISI), University of Bologna, Italy
a r t i c l e
i n f o
a b s t r a c t
Article history:
Volume is one of the most important features for the characterization of a tumour on a
Received 24 July 2014
macroscopic scale. It is often used to assess the effectiveness of care treatments, thus mak-
Received in revised form
ing its correct evaluation a crucial issue for patient care. Similarly, volume is a key feature on
3 December 2014
a microscopic scale. Multicellular cancer spheroids are 3D tumour models widely employed
Accepted 15 December 2014
in pre-clinical studies to test the effects of drugs and radiotherapy treatments. Very few methods have been proposed to estimate the tumour volume arising from a 2D projection
Keywords:
of multicellular spheroids, and even fewer have been designed to provide a 3D reconstruc-
Pre-clinical oncology
tion of the tumour shape. In this work, we propose Reconstruction and Visualization from a
Widefield microscopy
Single Projection (ReViSP), an automatic method conceived to reconstruct the 3D surface and
Multi-cellular tumour aggregates
estimate the volume of single cancer multicellular spheroids, or even of spheroid cultures.
Volume rendering
As the input parameter ReViSP requires only one 2D projection, which could be a widefield
Software application
microscope image. We assessed the effectiveness of our method by comparing it with other
Image processing
approaches. To this purpose, we used a new strategy that allowed us to achieve accurate volume measurements based on the analysis of home-made 3D objects, built by mimicking the spheroid morphology. The results confirmed the effectiveness of our method for both 3D reconstruction and volume assessment. ReViSP software is distributed as an open source tool. © 2014 Elsevier Ireland Ltd. All rights reserved.
1.
Introduction
Shape and volume are among the most relevant features for the characterization of the appearance of a tumour. A
correct shape assessment is at the basis of surgical planning [1], while the correct volume definition is essential to assess the effectiveness of cancer treatments and therapies and serves in the subsequent decision making process [2].
∗ Corresponding author at: Prof. Alessandro Bevilacqua, University of Bologna, Via Toffano 2/2, I-40125 Bologna, Italy. Tel.: +39 0512095409; fax: +39 0512095410. E-mail addresses: [email protected] (F. Piccinini), [email protected] (A. Tesei), [email protected] (C. Arienti), [email protected] (A. Bevilacqua).
http://dx.doi.org/10.1016/j.cmpb.2014.12.003 0169-2607/© 2014 Elsevier Ireland Ltd. All rights reserved.
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In particular, it has been shown that tumour volume is an important predictor of the clinical outcome of patients with different cancers [3]; it has also been used as a parameter to decide whether or not to exclude patients from curative chemotherapy/radiotherapy protocols [4]. Thus, the accurate estimate of tumour volume becomes fundamental for patient care. This importance of volume also holds at microscopic level. Among the different cancer models, cancer spheroids are particularly appealing, hence their growing popularity [5,6]. They are multicellular aggregates of “large” dimensions (i.e., up to 1 mm in diameter [7]) obtained in vitro from single cells from different cell lines [8]. Spheroids span the gap between monolayer cell cultures and whole-animal systems [9] and are used as a solid 3D model in a wide range of applications [10], e.g. to test drug dosages [11], radiotherapy treatments [12], and, more generally, to define new protocols for different tumour types [13]. Several methods and systems are employed to build spheroids [14]. Standard plastic well plates are typically used to maintain spheroids and analyze morphological and biological changes [15], then correlated to the effect of different drug dosages and radiotherapy treatments [16], also in high-throughput screens [17]. Tumour volume assessment is needed to obtain reliable data [18]. Nevertheless, due to the lack of accurate methods to estimate volume, the decrease/increase in the area of the maximum cross-section of spheroids is typically used to evaluate the effectiveness of treatments being evaluated [19]. However, this strategy may lead to several errors that can compromise the entire analysis because spheroids with same projection area can have very different volumes and vice versa (Fig. 1). In this work, we propose the Reconstruction and Visualization from a Single Projection (ReViSP) tool, a 3D volume rendering method conceived to reconstruct the 3D shape of spheroids, in addition to estimating volume by counting the voxels (i.e., volume elements) fully included on the 3D surface. It requires only one projection as the input parameter, which can be an image acquired with a widefield microscope, and automatically shows a 3D rendering of the spheroids. It is worth noting that the widefield microscope only provides a visualization from the top (or the bottom, in the case of an inverted microscope) which results in a single projection of a cross-section of the spheroid. The reconstruction of the whole 3D volume from a single projection is an extremely challenging task. In particular, the problem of reconstructing volumes from 2D projections arises in a large number of scientific fields, including computerized tomography, electron microscopy, X-ray microscopy, radiology, radio astronomy and holography [20]. Several reconstruction methods have been proposed since the beginning of the past century, but all require one sequence of cross-sections [21–23], or even a stereo image which provides depth information [24]. Nevertheless, a number of assumptions can help to estimate spheroid volume even from a widefield image only. First, due to gravity, spheroids lie on the bottom of the plate, typically in a stable equilibrium position. Accordingly, it is reasonable to assume that the image acquired depicts the widest cross-section of the spheroid. In addition, the similarity of the area values computed from as many images acquired after that a spheroid has reached its equilibrium position (after
being artificially moved away), suggests a spherical symmetry [25] of the spheroid with respect to the principal axis. This is also the reason why a sphere [18] or an ellipsoid [26] is often deemed a valid model to estimate spheroid volume. Starting from these considerations, we designed our method to reconstruct the 3D surface of spheroids by analysing the perimeter of the cross-section, computing the local distance between points perpendicular to the principal axis, and assuming a local circular symmetry in the z-dimension. We also devised a specific control to separately consider and reconstruct protuberances and protrusions, frequent in the spheroids of several cell lines (see, for instance, human lung cancer spheroids in Fig. 2, human epithelial carcinoma reported in Fig. 4 of [17], and rat hepatocytes reported in Fig. 1d of [8]). ReViSP is written in MATLAB (©, The MathWorks, Inc., MA, USA). Both the source code and a standalone executable version (i.e., not requiring MATLAB being installed) are freely available at: http://sourceforge.net/p/revisp, together with sample images that can be downloaded as well. To assess the effectiveness of our implementation, we propose a new comparative approach based on the analysis of home-made 3D objects built to mimic spheroid morphology. Starting from the microscopic analysis of several spheroids at the confocal microscope (Fig. 3), we built some representative macroscopic 3D models (in practice, synthetic spheroids) and then took shots of them by using a professional high-quality digital camera. Finally, we compared the volume values estimated using ReViSP with the real volume (technically, the ground truth), computed using water, a graduated cylinder and a precision balance. The results obtained confirmed the effectiveness of ReViSP in estimating the spheroid volume whilst also permitting a qualitative assessment of the reconstructed tumour shape. A very early version of this work, capable to work with single objects with fairly regular shapes, was proposed in [27]. In the present work, the reconstruction method was extended to include the class of spheroids with protuberances, this yielding a wider generality and an improved robustness of the method. Above all, the reconstruction algorithm was revised to work even when several spheroids are present in one image. Most important, this would also permit to deal with a culture of spheroids, acquired by building a mosaic of images [28]. In order to have a wide overview of spheroid morphology phenotypes, three different systems were employed to generate cancer multicellular spheroids. Then, new synthetic spheroids were employed in several experiments carried out to assess the proposed method’s performance in estimating the volume of spheroids, also with protuberances. ReViSP is distributed as an open source software tool endowed with a Graphical User Interface (GUI). In particular, it is provided as supplementary material by setting up a specific webpage also to host sample sets and future releases. The next sections are organized as follows. Section 2 presents a short overview of methods used to estimate biovolumes. Section 3 describes the materials used in the experiments. Section 4 provides a detailed description of the proposed method, the results of which are presented and discussed in Section 5. Finally, Section 6 reports the main findings of the work.
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Fig. 1 – Synthetic spheroids and ellipsoids of same volume. The solids in (a), (b) and (c) are spherically symmetric around the principal axis and lie on the x–y plane in a stable equilibrium position. Although they all have an identical volume, their projections (i.e., their cross-sections) have areas of different sizes. In particular, (d) is 3/4 of (e) and 2/3 of (f).
Methods for volume reconstruction from 2. a single projection Very few methods have been proposed in the literature to estimate tumour volume (starting) from a single projection image [29]. In particular, the first researchers to tackle this problem
were Gaylord and Clowes in 1906 [30] who used the formula of the ellipsoid (Eq. (1)) to approximate tumour volume starting from the computation of the maximum diameter M, passing through the centre of mass and its orthogonal diameter P:
VELLIPSOID =
· M · P2 . 6
(1)
Fig. 2 – Multicellular cancer spheroids obtained from lung cancer cells (line A549), built using different commercial systems (a–c), and characterized by a different number of protuberances (d–f). In particular, the spheroid in (a) was built using a RCCS-8DQ bioreactor (Synthecon, Inc.), in (b) with a Perfecta3D hanging-drop plate (3DBiomatrix, Inc.) and in (c) with a GravityPLUSTM hanging-drop plate (InSphero, Schlieren, Switzerland). The spheroids in (a–c) have a single small protuberance, those in (d) and (e) one and two large spherical protuberances, respectively, and the spheroid in (d) has several irregular-shaped protuberances. The scale bar in the lower-left corner of the images represents 250 m.
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Fig. 3 – Microscopic morphology of common spheroids. Confocal images were exported using Nikon NIS Elements AR software. (a) Spherical spheroid, (b) elliptical spheroid, (c) spheroid with irregular shape and one protuberance at the bottom right side. As can be seen in all the images, the maximum depth reached with the confocal microscope corresponds to about half the diameter of each spheroid. Scale bars represent 200 m.
Woglom [31], in contrast, proposed to use the formula of the sphere (Eq. (2)) by simply computing the average diameter D: VSPHERE =
· D3 . 6
(2)
Over time, several other formulas were proposed to estimate tumour volume, still supposing a sphere or an ellipsoid model. Tomayko and Reynolds, in the review work of 1989, compared 19 different formulas to estimate the tumour volume [32]. However, none of them seems to provide an accurate approach to obtain reliable data. In fact, the spheroids (and, in general, the tumours [33]) are typically characterized by a complex 3D morphology and there are only a few cases in which a sphere or an ellipsoid model can hold. Furthermore, even when the volume is estimated with a good approximation, these models provide a very unrealistic 3D representation that is not useful for clinical evaluations. No revolutionary breakthrough methods have been proposed since Sieracki et al., again in 1989, described an image processing method based on the object revolution around its maximum (straight) axis [34]. In practice, the authors analyzed the object perimeter by computing the local distance between points perpendicular to the maximum axis, as we do, a strategy that was better than computing single diameters and using a specific formula [35]. Although the method was designed to estimate the volume of bacterial cells, the authors only exploited the assumption of local spherical symmetry, which can also hold for multicellular spheroids. Along the same lines, Zeder et al. in 2011 proposed a new algorithm to estimate 3D volume based on the segmentation of the 2D object perimeter into triangles, subsequently approximated as 3D “half-cylinders” [36]. Although the algorithm was fairly complex, the authors provided a very user-friendly implementation. On the basis of the experiments carried out by the authors using synthetic bacteria, the method seemed to provide a good volume approximation, which however cannot be assessed visually, since the representation of the 3D surface of the object was not provided. Finally, in case the method
is used to estimate the volume of a multicellular spheroid, it must be pointed out that spheroids with more than one protuberance were not taken into consideration, thus potentially compromising the volume estimation. Above all, it is worth noting that the accuracy has not been validated with real-world objects. In conclusion, to our knowledge, ReViSP is the first software tool explicitly conceived to estimate the volume of single, or a culture of, cancer spheroids that also takes into consideration protuberances and provides a GUI to view and interact (i.e., to rotate, to scale, etc.) with the 3D reconstruction of the spheroid surface.
3.
Materials
3.1.
Cancer multicellular spheroids: creation
To analyze different spheroid phenotypes, we prepared several 96-well plates containing spheroids created using A549 cells, a commercial cell line derived from a primary lung cancer (American Type Culture Collection, ATCC, Rockville, MD, USA) commonly used to build spheroids [37]. Cells were grown in F12K medium (ATCC) supplemented with 10% fetal bovine serum (FBS, EuroClone, Milan, Italy), maintained as a monolayer in a 5% CO2 humidified atmosphere at 37 ◦ C, subcultured weekly using trypsin-EDTA solution (EuroClone) and checked periodically for mycoplasma contamination by MycoAlertTM Mycoplasma Detection Kit (Lonza, Basel, Switzerland). Finally, we used three different commercial systems to grow the multicellular spheroids: an RCCS-8DQ bioreactor [38] (Synthecon, Inc., Houston, TX, USA), and Perfecta3D [39] (3DBiomatrix, Inc., Ann Arbor, MI, USA) and GravityPLUSTM hanging-drop plates [40] (InSphero, Schlieren, Switzerland). A detailed description about cell preparation and the protocol used to create the spheroids with the bioreactor can be found in [10]. For the Perfecta3D and GravityPLUS hanging-drop plates, we followed the standard protocol provided by the manufacturers. The final size of the spheroids obtained with the bioreactor was between 500 m and 1 mm, while for the spheroids built with
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Fig. 4 – Different spheroid phenotypes. (a–d) Home-made 3D models of spheroids built to mimic the morphology of real multicellular cancer spheroids. (e–p) Spheroids built using an RCCS-8DQ bioreactor and (q–t) a Perfecta3D hanging-drop plate. (u–x) Spheroids built using a GravityPLUSTM hanging-drop plate and then maintained in culture by using a multi-well plate with conical-shaped wells (GravityTRAPTM , InSphero). In addition to the spherical (e), (i), (m), (q), (u) and ellipsoidal shapes (f), (j), (n), (r), (v), it is common to find also spheroids with “8”-like shape (g), (k), (o), (s), (w) or irregular shape (k), (l), (p), (t), (x).
the hanging-drop plates the size was between 250 m and 500 m. Although we expected to obtaining different spheroid phenotypes [13,41], the very high number of aggregates with irregular shapes was quite surprising (Fig. 4).
3.2.
Cancer multicellular spheroids: image acquisition
We imaged all the cancer spheroids using an inverted Olympus IX51 microscope equipped with a Nikon Digital Sight DS-Vi1 camera (CCD vision sensor, square pixels of 4.4 m side
length, 1600 × 1200 pixel resolution, 3-channel images, 8-bit grey level). Images were acquired in brightfield using an Olympus UPlanFl 4×/0.13na as a standard objective lens (Fig. 4). Then, to better analyze the different spheroid phenotypes, we acquired z-stack images (1024 × 1024 pixel resolution, 3-channel images, 12-bit grey level) representing several spheroids using a Nikon Eclipse Ti microscope equipped with an A1R confocal laser scanner (Nikon), temperature and CO2 controllers (Okolab, Ottaviano, Naples, Italy) and a Nikon Plan Fluor 10×/0.30na Ph1 DLL standard objective lens. In
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Fig. 5 – Home-made synthetic 3D spheroids. From left to right: Spheroid with one (G2A ), two (G2B ) and more protuberances (G2C ). The white scale bar represents 10 mm.
particular, the spheroids analyzed with the confocal microscope were stained using the LIVE/DEAD cell assay kit L-3224 (Molecular Probes, Inc., Leiden, The Netherlands) which is based on the simultaneous determination of live and dead cells with two fluorescent probes. Live cells are stained green by calcein AM due to their esterase activity, while the nuclei of dead cells are stained red by ethidium homodimer 1 (Fig. 3).
3.3.
Synthetic spheroids: creation
The real volume of a spheroid volume must be known in order to assess the accuracy of a volume estimating method. However, the volume of a cancer spheroid with a 1-mm maximum diameter is not easy to measure with a standard equipment, and synthetic objects designed by combining regular shapes through computer-aided drafting (CAD) software are typically used to overcome this problem [36]. Nevertheless, a “composite regular-shaped” object is not really capable of mimicking a spheroid characterized by several irregularities with respect to spheres and ellipsoids. Thus, starting from the observation of the morphology of the microscopic bio-spheroids, we proposed a validation approach based on synthetic 3D objects. In particular, we manually constructed some macroscopic 3D models (approximate scale 1:30) with morphologies very similar to those of cancer spheroids (Fig. 4a–d). To this purpose, we built up a first group (G1) of four representative synthetic 3D spheroids: one with a spherical shape (hereafter G1A , Fig. 4a), a second mimicking an ellipse/ovoid (G1B , Fig. 4b), a third with a “8”-like 3D shape (achieved by squeezing the central part of an ellipse, G1C , Fig. 4c), and a fourth of irregular shape (G1D , Fig. 4d). We then assessed the performance of all the reconstruction methods considered by building a second group (G2) of synthetic spheroids by simply stitching spheres of different dimensions to the main one to mimic spheroids with one (G2A , Fig. 5a), two (G2B , Fig. 5b) or more protuberances (G2C , Fig. 5c). All the synthetic spheroids were built using a commercial product called DAS (distributed by FILA, Pero, Milan, Italy), a malleable mineral-based polymer clay for modelling. Although we tried to make the objects spherically symmetric around the principal axis, being home-made they all had some degree of imperfection; this, however, helped to mimic real cancer multicellular spheroids. Finally, we indirectly
computed the real volume of the 3D models, first by experimentally estimating the density of the DAS using a graduated cylinder and precision balance (Denver Pinnacle PI-114, Denver Instrument, Bohemia, NY, USA) and then by weighing them all. This procedure also offers the possibility to perform repeated measures of the real volume of 3D objects, with a great accuracy, this being fundamental to allow a fair comparison between the different methods tested and subsequently ranking based on their estimated volume.
3.4.
Synthetic spheroids: image acquisition
We compared the different methods by placing the DAS objects in a stable equilibrium position on a flat table and acquiring an image for each object with a manual high-quality, professional Nikon D800 reflex camera (CMOS vision sensor, square pixels of 4.7 m side length, 5520 × 3680 pixel resolution, 3-channel, 8-bit images) fixed perpendicularly to the table through a dedicated tripod. A telecentric commercial Nikon AF-S VR Micro-Nikkor 105 mm f/2.8G was used as an objective lens. The spatial resolution factor of the camera was experimentally calculated by keeping the set-up of the acquisition system unchanged and acquiring images using several squared test-pieces of known dimension.
3.5.
Revisp software tool
ReViSP is freely distributed as an open source software tool. In particular, the 32-bit and 64-bit standalone executable versions of ReViSP and the source code can be found at http://sourceforge.net/p/revisp. Also, all the images used in the experiments are available on demand. ReViSP is endowed with a simple GUI (Fig. 6) that makes it particularly userfriendly. Nevertheless, it is worth noting that the executable versions can be also executed as a console application, via command line. Consequently, it can be employed in all programmes that include scripting facilities. The only input parameters required are the destination folder where the binary masks of the spheroids to be reconstructed are stored, and the name of the images to be analyzed. It is also possible to define the conversion coefficient m/pixels to automatically obtain the volume in cubic micrometres (provided in voxels
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masks handled through the “ROI manager” plug-in of ImageJ [42], an open source image processing software widely used in microscopy for different purposes [43]. Notably, to obtain fine 3D rendering as a final result, it is important to start from the mask relating to the widest cross-section of the spheroid. To this purpose, several images are typically acquired by scanning the object along the z-axis, and the final fully focused 2D image is reconstructed by merging the content of the different images [44] previously corrected for radial fall-off attenuation of the image intensity [45]. Once the binary mask of the spheroid has been obtained, it is automatically divided into sections comprising the main core of the spheroid and any protuberances that are present (specific details are provided in Section 4.3), after which all the single objects are separately reconstructed and merged to obtain fine 3D rendering. In case of spheroids with a necrotic core, we suggest providing as the input of ReViSP the entire mask of the spheroid and the mask referring to the necrotic core, and then compute the “effective” volume as their difference.
4.2. Fig. 6 – A snapshot of the ReViSP GUI main window. Each button is coupled with a help menu, thus rendering the ReViSP particularly user-friendly.
by default). Although the algorithm has not been optimized, the time required to reconstruct one spheroid is compliant with online processing. We performed the experiments using an entry-level PC (Intel Core i5, CPU 2.27 GHz, 4 GB RAM). In particular, we assessed the computational performance by measuring the elapsed time needed to analyze a sequence of 100 very large binary masks (5520 × 3680 pixels each) stored on the hard disk. The total time required was about 30 min, with an average of 18.6 s per mask. Finally, in addition to the ReViSP source code, the MATLAB functions we devised to compute the volume according to Eqs. (1) and (2) (referring to the sphere and the ellipsoid model, respectively) are also provided as supplemental material (http://sourceforge.net/p/revisp) for the sake of completeness.
4.
Methods
Fig. 7 outlines the approach conceived to reconstruct and visualize the 3D surface of the spheroids whilst estimating their volume. In particular, ReViSP, like all the other methods compared in the experiments, only requires the binary mask (black and white image with 0 for the background and 1 for the foreground) of the spheroid to be analyzed as the input parameter. The 3D surface of each spheroid is then reconstructed by analysing the spheroid perimeter, subdividing main core and protuberances as different objects. The volume consists of the number of voxels contained in the whole 3D surface, provided in output. More details on the different steps of the proposed methods are provided in the next subsections.
4.1.
Image pre-processing
Starting from the RGB images acquired, we manually segmented the synthetic spheroids to obtain the black and white
Image rendering
All the single objects (meant as parts) of the binary mask are separately processed one at a time according to the following steps: 1. detection of the maximum straight diameter passing through the centre of mass of the object (red dashed line in Fig. 8a); 2. rotation of the object to have the maximum diameter parallel to the y-axis of the image and for each y-position, calculation of the x-length of all the perpendicular segments; 3. creation of an x-y map (called 3D map) in which the local height (in pixels) of the 3D surface is reported in relation to the x-y plane passing through the centre of mass of the spheroid and parallel to the image plane (Fig. 8b). The map is built by fitting, for each y-position, a 3D circle with diameter equal to the length of the local x-segment; 4. 3D surface rendering of the object (Fig. 8c) by considering the x-y values of the 3D map as the height (in pixels) of both halves of the spheroid where the top and the bottom parts are considered perfectly symmetric with respect to the x-y plane passing through the centre of mass of the spheroid and parallel to the image plane.
4.3.
Analysis of protuberances
Several brightfield images of cancer spheroids with protuberances are shown in Fig. 2. We devised a strategy aimed at separating the main spheroid from the protuberances to obtain fine 3D reconstruction. It is important to remember that these “appendices”, if not adequately analyzed, may compromise the entire reconstruction process (Fig. 9). Starting from the concavity points of the spheroid perimeter, we used the watershed transform [46] to detect and segment the protuberances. Once the labelled binary mask subdividing protuberances and main core of the spheroid had been obtained (Fig. 9c), we considered every single object (meant as single parts of the binary mask) following the four steps
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Fig. 7 – ReViSP flow-chart. ReViSP requires in input the binary mask of the spheroids to be analyzed. The 3D surface of each spheroid is then reconstructed by separately analysing main core and single protuberances. Finally, the volume is estimated by counting the voxels encompassed by the whole 3D surface.
reported in Section 4.2. At the end, to estimate the final volume (in our approach the final volume estimated does not simply correspond to the sum of the volume of the single parts) and provide a realistic connection between the different parts of the spheroid, we connected the single 3D parts reconstructed using cylindrical connections whose base has as the same diameter as the length of the contact line between the main core of the spheroid and the single protuberance, and the height locally adapted to follow the curvatures of the two objects to be connected. Fig. 9e and f shows as one of the two protuberances of the object reported in Fig. 9d appears before (Fig. 9e) and after (Fig. 9f) being connected to the main core of the object through the cylindrical connection.
4.4.
the culture of spheroids is automatically obtained by summing the volume of each single spheroid. In particular, when using the ReViSP GUI, the volume of both single spheroids and the whole culture are reported through a pop-up window.
4.5.
Starting from the binary mask obtained for each DAS model reported in Figs. 4a–d and 5, we computed the volume with ReViSP and with four other methods, hereafter named as ELLIPSOID (Eq. (1)), SPHERE (Eq. (2)), SIERACKI [34] and ZEDER [36]. Finally, we determined the absolute percentage errors (E%) between the ground truth (GT) volume and the values estimated (V) with the different methods, according to Eq. (3) [34]:
Analysis of a spheroid culture E% =
ReViSP was conceived to permit the analysis of a culture of spheroids, i.e. a group of spheroids imaged together. Whether the spheroids are contained in a single flask or a well of a plate, an automatic mosaicing software tool, e.g. Autostitch [47] or MicroMos [48], can be used to acquire a composite widearea image (namely, a mosaic) that is representative of the whole sample. Once the mosaic of a whole culture has been obtained (Fig. 10a), the single spheroids must be segmented as reported in Section 4.1. No additional prior information is required. The single 3D surfaces of the spheroids are automatically reconstructed one at a time by following the steps reported in Section 4.3, and 3D rendering of the whole culture is shown as the output (Fig. 10b). Finally, the total volume of
Comparison approach
5.
|V − GT| · 100. GT
(3)
Results and discussion
Our experiments aimed to compare the quality of ReViSP with other approaches used to estimate the volume of an object starting from a single projection image. The 3D reconstructions obtained with ReViSP for the objects described in Section 3.3 and illustrated in Figs. 4a–d are shown in Fig. 11a–d. Table 1 reports the E values computed with the different methods according to Eq. (3). The best results are highlighted in bold. With regard to G1A , all the methods yielded an error lower than 2%, indicating that all the methods tested accurately
Fig. 8 – Method proposed to reconstruct the 3D surface starting from the binary mask of the object. First, the direction of the y-axis of the image plane is assigned to match the direction of the maximum axis of the object (a). The x-y 3D map is then computed by fitting a 3D circle for each y-position (b). Finally, the 3D surface is reconstructed (c) considering the x–y values of the 3D map as height in pixels of half of the object (symmetric with respect to the x–y plane passing through the centre of mass of the spheroid).
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Fig. 9 – Separate analysis of the protuberances is fundamental to obtain reliable 3D reconstruction and volume assessment. (a) Binary mask of a spheroid with two spherical protuberances. The principal axis is shown in red. (b) 3D reconstruction obtained without separately analysing the protuberances. The 3D surface was obtained by simply assuming a local spherical symmetry around the principal axis. (c) Labelled binary mask obtained using the approach we proposed to detect and segment the protuberances. (d) 3D surface obtained by processing (a) without physically connecting the single parts (e). (f) A detail related to the more realistic 3D reconstruction attained by using cylindrical connections to “stitch” protuberances to the main core of the object.
estimated the volume of a spheroid when its shape was close to a real sphere. Conversely, for all the remaining 3D models of G1, ReViSP, SIERACKI and ZEDER yielded similar results (maximum percentage error E% always lower than 8%) and always performed better than SPHERE and ELLIPSOID. In particular, SPHERE had an error rate lower than 10% for G1D , but very high
E values for both G1B and G1C . Similar results were obtained for ELLIPSOID which, however, also showed the worst result (33% error for G1C ). In analysing the average E% (Table 1, last row), it can be seen that ReViSP was generally the best model and that the performance of SIERACKI and ZEDER was comparable with ours (difference in average E% lower than 2%). This was
Fig. 10 – Culture of spheroids contained in a single well. (a) Mosaic of a whole well of a 96-well plate. The mosaic was obtained first by manually acquiring 40 overlapping images to cover the entire well, then using MicroMos, which is capable of merging a sequence of previously acquired overlapping images into a high accurate mosaic stitch. Once the mosaic was obtained, it was manually segmented to be processed by ReViSP. The visualization of the 3D surface of the spheroid culture is shown in (b).
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Fig. 11 – 3D surfaces relating to (a) G1A (Fig. 4a), (b) G1B (Fig. 4b), (c) G1C (Fig. 4c), (d) G1D (Fig. 4d), (e) G2A (Fig. 5a), (f) G2B (Fig. 5b), and (g) G2C (Fig. 5c). The surfaces were reconstructed using ReViSP.
Table 1 – E% values obtained by the different methods using the G1 synthetic spheroids. SPHERE G1A G1B G1C G1D Average E% G1
0.69 23.97 21.91 9.00 13.89
ELLIPSOID
SIERACKI
1.99 8.15 33.12 16.10 14.84
1.09 2.45 4.34 3.79 2.92
ZEDER
ReViSP
0.74 2.01 4.96 7.34 3.76
1.26 2.08 1.65 3.55 2.14
ZEDER
ReViSP
7.16 9.35 10.66 9.06
1.02 5.89 6.55 4.49
For each row the best value (lower E%) is reported in bold.
Table 2 – E% values obtained by the different methods using the G2 synthetic spheroids. SPHERE G2A G2B G2C Average E% G2
17.89 11.79 19.23 16.30
ELLIPSOID
SIERACKI
23.03 6.45 162.96 64.15
5.28 13.77 37.88 18.98
For each row the best value (lower E%) is reported in bold.
further confirmed by analysing the specific cases: ReViSP results were either the best (for G1C and G1D ) or differed at most 1% from those of the best method in each case. Similarly, the performance of SIERACKI and ZEDER never differed more than 5% from that of the best method. Overall, ZEDER, SIERACKI and ReViSP can be considered comparable methods, with an average E% lower than 5%, much lower than the average errors achieved by SPHERE and ELLIPSOID (higher than 10%). The scenario changed when we considered the synthetic 3D models of G2 that mimic spheroids with protuberances. Fig. 11e–g shows the 3D reconstructions obtained with ReViSP for the objects shown in Fig. 5, while Table 2 reports the E% values computed for the different methods. ReViSP was always the best method. Moreover, it was the only method that consistently achieved results lower than 8%, in specific cases and on average. Considering the latter (Table 2, last row), it is worthy of note that SIERACKI and ZEDER were no longer comparable with ReViSP, and that only ZEDER
was clearly superior to SPHERE and ELLIPSOID. In particular, ELLIPSOID showed a very poor performance (E% higher than 162% for G2C ), indicating that the estimation of perpendicular diameters in spheroids with protuberances can completely misdirect volume estimation.
6.
Conclusions
In this work, we introduced ReViSP, a user-friendly software tool conceived to reconstruct and visualize the 3D surface of a culture of cancer spheroids by using single images or mosaics acquired with a widefield microscope. The only prior information required is the binary mask of the objects to be segmented and the 3D reconstruction exploits only the morphological appearance of the spheroids. Once 3D rendering of the object has been obtained, the volume can be automatically estimated by counting the voxels inside the surface.
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As a matter of fact, the approach we proposed could also work for a number of in vivo tumours, although the assumptions regarding local sphericity and stable equilibrium make our approach suitable mainly for multicellular aggregates. To compare the accuracy of ReViSP with previously proposed volume-estimating methods, we used a new approach based on home-made 3D models built by mimicking the morphology of cancer multicellular spheroids. The results obtained by ReViSP were very promising and superior to those of the other methods tested (average error lower than 5%). It is worthy of note that the 3D rendering of the objects analyzed can be visualized with ReViSP, thus permitting a visual assessment of the outcome. However, the most important finding to emerge from our study was the fact that some commonly used methods to estimate the volume of a spheroid are usually characterized by a very high average error (higher than 10%) which could completely mislead drug-testing analyses, especially if the cell lines used yield spheroids with protuberances. In conclusion, meanwhile sensitizing the reader about this problem, we provide an effective solution to computing the volume of cancer spheroid cultures by exploiting a single 2D projection. Our results confirm that the approach may be suggested as the most likely candidate for the new gold standard method to estimate spheroid volume when only a single 2D projection is available. The standalone executable versions of ReViSP are freely available at: http://sourceforge.net/p/revisp, as well as the source code. Since ReViSP can be also executed as a console application, this allows it to be included in batch files or scripts so to be easily employed together with the software of automatic plate readers endowed with a digital camera (e.g., ImageXpress® Micro Widefield High Content Imaging System, Molecular Devices, Silicon Valley, CA, USA; Dainippon SCREEN Cell3iMager, InSphero).
Conflict of interest The authors declare that they have no conflict of interest.
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Acknowledgements We thank FILA (Pero, Milan, Italy) for providing the information on the DAS material; 3D Biomatrix Inc. (Ann Arbor, Michigan, USA) and InSphero (Schlieren, Switzerland) for supplying the hanging-drop plates to build the spheroids; Alessandro Gherardi (Computer Vision Group, University of Bologna) and Nicola Garavini (Faenza, Ravenna, Italy) for image acquisition support; Enrico Lucarelli and Serena Duchi (Osteoarticular Regeneration Laboratory, Rizzoli Orthopaedic Institute, Bologna) for analysing the 3D shapes of the spheroids using a confocal microscope; Gráinne Tierney (IRST, Meldola, Italy) for editorial assistance and English revision of the manuscript.
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