Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images

Medical Image Analysis 13 (2009) 871–882

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Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images László Ruskó *, György Bekes, Márta Fidrich GE Hungary Healthcare Division, Szeged, Szikra u. 2, 6725, Hungary

a r t i c l e

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Article history: Received 20 February 2008 Received in revised form 16 July 2009 Accepted 16 July 2009 Available online 23 July 2009 Keywords: Automatic segmentation Contrast-enhanced CT Multi-phase images Region-growing Liver contouring

a b s t r a c t Segmentation of contrast-enhanced abdominal CT images is required by many clinical applications of computer aided diagnosis and therapy planning. The research on automated methods involves different organs among which the liver is the most emphasized. In the current clinical practice more images (at different phases) are acquired from the region of interest in case of a contrast-enhanced abdominal CT examination. The majority of the existing methods, however, use only the portal-venous image to segment the liver. This paper presents a method that automatically segments the liver by combining more phases of the contrast-enhanced CT examination. The method uses region-growing facilitated by pre- and post-processing functions, which incorporate anatomical and multi-phase information to eliminate overand under-segmentation. Another method, which uses only the portal-venous phase to segment the liver automatically, is also presented. Both methods were evaluated using different datasets, which showed that the result of multi-phase method can be used without or after minor correction in nearly 94% of the cases, and the single-phase method can provide result comparable with non-expert manual segmentation in 90% of the cases. The comparison of the two methods demonstrates that automatic segmentation is more reliable when the information of more phases is combined. Ó 2009 Elsevier B.V. All rights reserved.

1. Introduction Many clinical applications for computer aided diagnosis and therapy planning require 3D medical images to be segmented. For example, planning of liver tumor embolization, ablation, and surgical resection require precise segmentation of the liver from CT images. Due to the complex shape and the large size of this organ, the manual segmentation is time consuming. In order to increase the efficiency of the clinical work, automatic segmentation methods are needed. Most of the automated liver segmentation methods are based on region-growing (Pohle and Toennies, 2001), active contour or surface (Bekes et al., 2007; Heimann et al., 2006), level-set (Pan and Dawant, 2006), or voxel classification algorithms (Pham et al., 2007), which were adapted to liver segmentation and connected with some pre- and post-processing operations (Soler et al., 2000). Since the intensity as well as the boundary gradient of the liver varies from one part to the other, the methods are usually constrained with statistical shape (Lamecker et al., 2004; Slagmolen et al., 2007; Weese et al., 2001) or volume model (Park et al., 2003; Zhou et al., 2006). Due to the large variation in the liver’s shape, the benefit of using an average model (or a couple of typical models) is limited. Further* Corresponding author. Tel.: +36 23 410 173; fax: +36 62 546 824. E-mail address: [email protected] (L. Ruskó). 1361-8415/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.media.2009.07.009

more, the registration of the model to a specific exam can be time consuming. This must be carefully considered for clinical applications. In case of computer aided diagnosis or radiotherapy planning, physicians have to process several cases sequentially. Since the automated background processing of medical images is not widely used in clinical practice yet, a time consuming segmentation method would decrease the efficiency of the physician. The workshop (Heimann et al., 2009) called ‘‘3D segmentation in the clinic: a grand challenge” of the MICCAI 2007 conference gave good opportunity to compare the recently developed methods for liver segmentation. In this contest, the liver was segmented using only the portal-venous image of the multi-phase contrastenhanced CT examination. The results demonstrated that the average clinical case can be handled by the current automatic methods with outstanding precision, but in extreme pathological cases (large lesions) most of the methods fail. Various automated methods were evaluated at this workshop. van Rikxoort et al. (2007) presented a method that is based on statistical voxel classification using a probability liver model. The method of Kainuller et al. (2007) uses a statistical shape model that is combined with a constrained free-form model. Chi’s segmentation method (Chi et al., 2007) integrates a rotational template matching, and k-means clustering followed by rib cage area local edge enhancement, with a gradient vector flow geometric snake. Schmidt et al. (2007) presented a system that allows defining a set of rules,

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based on which abdominal organs are segmented (including the liver) using simple functions (like region-growing, or morphological operators). The method of Furukawa et al. (2007) uses maximum a posterior probability estimation for rough liver extraction subsequently refined with a level-set method. Seghers et al. (2007) presented an active shape model method, wherein multiple local shape models are used. The method of Susomboon et al. (2007) uses intensity-based partition, texture-based classification, probability model, and thresholding to segment the liver. Slagmolen’s et al. (2007) presented a method that incorporates atlas using nonrigid registration. The method of Saddi et al. (2007) uses a statistical shape model for global-to-local shape matching. The method (Ruskó et al., 2007) we submitted to this workshop uses neighborhood-connected region-growing, which incorporates the local neighborhood of the voxel, instead of using only its value. Consequently, the result is more robust than from a standard regiongrowing. In order to decrease under- or over-segmentation, the method is extended by several pre- and post-processing steps, which incorporate anatomical knowledge. According to the MICCAI workshop’s evaluation, our method handled the typical clinical case precisely, but it was challenged by the extreme cases. Based on its good average performance it won the second prize of the onsite competition1 in the fully automated category (Heimann et al., 2009). The evaluation of the workshop did not take the running time in account. The only constraint was to segment 10 cases within 3 h. According to the values reported in literature (Nakayama et al., 2006) the manual segmentation of the liver takes about 30 min, which can be reduced below 10 min (Hermoye et al., 2005) using semi-automated tools. Although, computation demanding automated algorithms could be executed as background process, this feature is not widely available in the current clinical systems. In contrast to the surgical planning (that is time consuming, anyway) there are some applications (e.g. computer aided tumor detection), which do not require liver contouring, but can be facilitated with that (visual enhancement, or automated detection). In such cases, physicians would not use a feature that is based on a time consuming segmentation method. Thus, only methods having running time significantly less than 10 min can be applied in wide range of clinical applications. From this point of view our method, (taking about half a minute per case) was considered favorably. The precise results demonstrated that, using basic image processing concepts, our method can provide competitive segmentation results much faster than do the more sophisticated methods, which took at least 10–15 min to process one case. Most of the published liver segmentation methods use the portal-venous image of the contrast-enhanced CT exam. In the clinical practice 2–4 images are usually acquired. As well as the portal-venous, the early arterial image is always available, moreover, the native and the late-parenchymal images are also acquired in many cases. The contrast uptake of a given tissue type (e.g. liver parenchyma, tumor, etc.) is characteristic of the different phases and depends on many variables, including patient’s blood circulation, acquisition timing and type of contrast agent. Thus the quality of the portal-venous image can vary significantly between patients and clinical sites. A method that uses more phases, does not depend so much on external circumstances, so it can be widely used in clinical practice. Use of more phases to segment anatomical structures has been already introduced. Saitoh et al. (2002) presented a liver segmentation that automatically delineates portal vessel structure using the portal-venous and the native images, and segments the liver volume starting from the vessels. The paper of Duda et al. (2006)

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http://sliver07.isi.uu.nl/miccai.php.

describes a method for automatic liver lesion detection and classification from multi-phase CT images. The method of Shimizu and Nawano (2004) extracts a rough liver region (from triple-phase images) that is subsequently refined with a level-set method to get precise segmentation. References (Zhang et al., 2004, 2007) discuss automated liver volume and lesion segmentation that is based on the subtraction of portal-venous and native images. The above methods require that the input images be accurately registered before segmentation, so their results depend significantly on the registration quality. The precise registration of a multi-phase abdominal CT is time consuming, so the clinical utility of these methods is limited. Our primary goal was to develop a method that does not depend on a specific acquisition protocol and performs precise segmentation in less than a minute, so it can be used in a wide range of clinical applications. In this paper we present and compare two methods for liver segmentation. The first, referred as the multi-phase method, is able to combine the information of all available phases. The second, referred as the single-phase method, uses only the portal-venous image. We compare the results of these methods and show that it is beneficial to use more phases.

2. Automatic liver segmentation for multi-phase CT images The idea behind our multi-phase method is to segment the liver based on its characteristic contrast uptake. This approach requires the different phases to be accurately registered, which is a time consuming process. In order to do so efficiently, some steps are performed with the multi-phase image, and others with each

Fig. 1. Outline of the multi-phase method. Some of the steps are performed on the multi-phase image, while others on each phase separately.

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phase separately (Fig. 1). In the first step (Section 2.1) an initial segmentation is created using all phases. Since the liver is a large organ, its volume overlaps significantly in all phases. This property makes it possible to determine large part of the liver without registering all phases accurately. The following three steps are performed on each phase separately. The input of these steps are the original input images and the result of the initial segmentation. Firstly, the heart is separated from the liver (Section 2.2) to prevent large over-segmentation. Then, a neighborhood-connected regiongrowing (Section 2.3) is performed. Finally, the result of the regiongrowing is corrected, so that it involves the hyper- or hypodense regions of the liver. After the segmentation is available for each phase, the results are registered, which allows computing the final result as a combination of all phases (Section 2.5).

2.1. Initial segmentation to determine seed region for all phases In this section we describe, how to determine a connected set of voxels that represents a significant part of the liver volume. The method is based on the analysis of the multi-phase histogram of the input images. It is difficult to determine the intensity range of the liver using histogram analysis if the phases are considered separately. Some organs have similar intensity to the liver in a given phase. Furthermore, the intensity of the liver in a particular phase can vary significantly, depending on acquisition timing, type of contrast agent and patient’s blood circulation. Fig. 2 shows the average intensity of the liver in the arterial and the portal-venous phase for 20 multi-phase CT exams. The intensity at the arterial phase of one exam (13) can be higher than the intensity at the venous phase of another exam (4). The intensity difference between the arterial and venous phases varies also significantly, exams (6 and 18). In order to find the intensity range of the liver automatically, all phases are combined. Consider a two-phase (arterial, portal-venous) abdominal CT such that the phases are not precisely registered. In clinical practice the arterial and the portal-venous images represented in a patient-based coordinate system. Even if the pixel size or the slice thickness changes between the phases, the data description allows a quick and rough alignment of the different phases. Fig. 3 demonstrates the fusion of two phases without precise registration. The liver is easier separable from surrounding tissues if two phases are considered: liver parenchyma is green, muscles are dark brown, arteries are orange, and spleen is light brown. Consider the two-phase joint histogram of the input images. This function assigns high probability to an intensity pair ði1 ; i2 Þ if the intensity of many voxels is equal to i1 at the arterial and to i2 at the portal-venous phase. Even if the phases are not accurately registered, some peaks are clearly separated on the histogram (Fig. 4). Each of these maxima represents a specific tissue type (set of voxels, with similar contrast uptake). The maximum representing the liver parenchyma can be found easily. Consider only voxels having intensity in the range [50, 250] HU at both phases (excluding air and fat from the analysis). Since the liver is the largest organ in the abdomen, the highest

Fig. 2. The average intensity of the liver in arterial and portal-venous phase for 20 contrast-enhanced CT examinations.

peak of the two-phase histogram always represents the liver parenchyma (to make sure, the histogram is computed incorporating voxels located in the right half of the abdomen). When the largest maximum ðm1 ; m2 Þ of the two-phase histogram is found, all intensity pairs, which represent similar contrast uptake are considered. The similarity is defined in the following way:

ði1 ; i2 Þ ’ ðm1 ; m2 Þ

if ji1  m1 j < 20 HU; and ji2  m2 j < 20 HU; and 0:2  hðm1 ; m2 Þ 6 hði1 ; i2 Þ;

where hði1 ; i2 Þ represents the histogram value at ði1 ; i2 Þ. Then, a binary image is created so that 1 is assigned to each voxel where the contrast uptake is similar to ðm1 ; m2 Þ. Finally, the largest connected component of the binary image is determined and subsequently eroded with a kernel having 5 mm radius. The result set of voxels (Fig. 4) represents a connected area inside the liver, which can be used to initialize the neighborhood-connected region-growing (Section 2.3). The concept described in this section was applied to three contrast-enhanced phases (in many cases the native, the arterial, the portal-venous, and the late phases are all acquired). In such case a three-phase histogram is computed from the input series, and the liver parenchyma is represented by an intensity triplet. Our tests showed that using three phases provides more reliable initial segmentation, but 2 contrast-enhanced phases are enough to determine seedpoints robustly for the region-growing. 2.2. Liver–heart separation In portal-venous and late phases the heart has nearly the same intensity as has the liver. In order to prevent the segmentation from leaking into the heart, a method that separates these two organs was developed. The heart is separated from the liver by means of connecting the bottom of the left and the right lung with a smooth surface. Firstly, the lungs are segmented. Then, for each coronal slice a curve is found of minimal length, which connects the two lungs encountering large gradient values. This set of curves defines a surface that is used to separate liver from heart. The lung segmentation is performed as follows. Starting with the topmost slice both lungs are segmented on the basis of the intensity of air. To find seedpoints for the left and the right lung on the topmost slice, the largest connected region, wherein the intensity of voxels is higher than 400 HU is determined. This region represents the body. Inside the body the largest connected air region for the left and the right side is determined separately. The left and the right lungs are subsequently segmented using a standard 3D region-growing method (Fig. 5). After the lungs are segmented, each coronal slice of the CT image is processed. The bottom contours of the right and the left lungs are first determined. Then, the leftmost point (L) of the right lung and the rightmost point (R) of the left lung is computed (Fig. 5). L and R is connected as follows. Moving from L to R on slice y, in each column x the location of the largest gradient value located in the c ¼ 5 mm local environment of the previous point ð½zx1;y  c; zx1;y þ cÞ is chosen. When the vicinity of R is reached, the current point is connected with R by a line. When the L–R curve is available for each coronal slice, the surface separating the liver and heart is calculated by averaging the curves located in the neighboring slices. For a given coronal slice y the surface point at column x is defined as ðzx;y1 þ zx;y þ zx;yþ1 Þ=3. Finally, for each coronal slice the voxels located above the surface are set to a high intensity value (3000), so that the segmentation cannot leak into the heart. The right image of Fig. 5 demonstrates the results of liver–heart separation.

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Fig. 3. Fusion of two contrast-enhanced phases. The arterial (left) and venous (middle) images are combined using red and green palette, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4. Joint histogram of a two-phase image (middle). The well separable maxima represent different tissue types. Based on the location of the largest maximum the liver parenchyma can be separated from surrounding tissues (right).

Fig. 5. Separation of liver and heart. After segmenting the lungs within the body (left), the left and the right lung are connected with a curve on each coronal slice (center). The set of coronal curves define a surface (right) that prevents the region-growing to leak into the heart.

2.3. Neighborhood-connected region-growing Neighborhood-connected region-growing (NCRG) is used to segment the liver parenchyma. This method requires some seedpoints and an intensity range of voxels to be segmented. It starts from the seedpoints, and in each iteration a new voxel is added to the region if all voxels in its neighborhood satisfy the intensity condition. After a voxel is added to the region, its six neighbors are also processed. The method stops if no voxels remain to be processed. This method was chosen because it is fast and it can precisely segment regions, wherein the intensity is nearly homogeneous. In order to reduce noise, Gaussian filter with a kernel having a radius of 1.5 mm is applied. The set of seedpoints is determined as in Section 2.1, and the intensity range is computed on the basis of the initial segmentation as follows. The histogram of the initial region is created, and the modus ðMÞ and the standard deviation of the intensities lower ðLÞ and higher ðHÞ than the modus are computed. The intensity range is defined as ½M  c  L; M þ c  H, where c ¼ 3 is an empirical constant. The probability of over-segmentation can be reduced by using a larger neighborhood. According to our experiments, over-segmentation is significantly reduced using a 5 mm radius sphere. Using such a big environment increases the number of voxels processed in one iteration. In order to preserve efficiency, the CT image is resliced (by omitting slices), so that the slice spacing is greater than

2 mm. In case of an exam having typical voxel spacing, the environment consists of about 400 voxels. The larger the radius of the sphere is, the more sensitive to noise the method is. In order to make the segmentation more robust, a tolerance was introduced for testing the intensity condition. Based on our experiments this tolerance was set to 2%. This means that the intensity condition is true if at least 98% of the voxels located in the neighborhood satisfies that. Note that the NCRG stops 5 mm before reaching the boundary of the liver. That is why, dilation using a sphere with 5 mm radius is applied to the result of the region-growing. 2.4. Correction of hypo- and hyperdense regions In some cases the liver is under-segmented near the right lung, where many voxels have intensity below the accepted range. This problem is corrected by an additional segmentation that allows lower intensity range in the region located between the segmented liver and the right lung. In order to find the target region, the surface of the segmented liver and the right lung is analyzed. Firstly, the surface voxels for the right lung are determined and the surface normal is calculated for each of them. If the normal vector of a surface voxel points toward a liver voxel that is closer than 20 mm, the surface voxel is labeled. Each labeled lung surface point is connected with the corresponding liver voxel using a discrete line. Then the 5 mm local environment is calculated for all lines,

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which defines a closed connected region between the liver and the right lung. Based on this region a new intensity interval is calculated, which is used by an additional NCRG. This method starts from liver surface points and is limited to the target region, so it cannot cause over-segmentation in other parts of the liver. An example for additional segmentation in the right lung can be seen in Fig. 6. The result of the NCRG usually does not involve voxels belonging to the portal vein because they have significantly higher intensity. In clinical practice, the vessel is considered as part of the liver as long as it is completely surrounded by liver parenchyma. In order to reduce such under-segmentation, opened cavities having diameter nearly equal to the average diameter of the portal vein are detected and filled. Firstly, the contour of the segmented liver is determined, and the surface normal for each contour voxel is calculated. Then, each surface voxel is labeled, wherein the distance of another liver voxel in the direction of the normal vector is nearly equal to the average diameter of the portal-vein. Subsequently, the liver is dilated at each labeled surface voxel using a sphere, the radius of which is equal the average radius of the portal vein. This approach (in contrast to a standard morphological opening) fills only the holes due to under-segmented vessels without smoothing other parts of the liver surface. Fig. 7 demonstrates the result of this post-processing step. In addition, the under-segmentation due to lesions is reduced using 3D cavity filling. In this step, all background voxels, which are not 3D connected to the largest background region, are added to the liver volume. Note, that this step cannot fill lesions located on the boundary of the organ. 2.5. Register and combine all phases Although NCRG is used, over-segmentation can occur in different parts of the liver. Most of the affected areas (muscles between ribs, pancreas, stomach, bowels) cannot be described with simple rules due to the large variety of their location and shape. A neighboring organ is very unlikely to have similar intensity to the liver in all phases. Thus, combining the segmentation results belonging to the different phases can reduce the probability of over-segmentation. Due to the patient’s respiration, the organs located close to the lung move and deform considerably between two phases (Fig. 8). It can be clearly seen (especially on the coronal slices) that the liver is shifted down significantly due to respiration. This phenomenon is characteristic of the multi-phase abdominal CT images, so registration is needed before combining the different phases. The inter-phase registration is confined to the environment of the liver. Since the registration is time consuming for a two- or three-phase abdominal CT image, only the segmented binary volumes are registered. For further optimization, the liver volumes are resampled using isotropic voxel size (5 mm  5 mm  5 mm). The registration computes an affine transformation in two steps. First, a translation transform is computed. This step is initialized

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such that the weight center of the moving image is shifted into the weight center of the fixed image. Subsequently, an affine transform is calculated. At initialization the matrix of the affine transform (representing rotation, scale, and shear) is equal to identity, the translation is equal to the result of the previous step, and the rotation center is set to the weight center of the moving image. In both registration steps the squared difference is used as similarity metric, and gradient descent method is applied to find the optimal transformation. Fig. 9 demonstrates the result of the inter-phase registration. After the segmented volumes are registered, the result can be defined as an arbitrary function of the segmented as well as the original grayscale images. All registered images must be resampled to the size and voxel spacing of the reference image. The result is defined as the intersection of all segmented images, so only the binary volumes are resampled. The number of voxels of the output image can be very large (e.g. 512  512  600) if the abdominal CT acquisition covers large part of the pelvis or the chest, or the slice thickness is under 1 mm (both are very common in clinical practice). Applying the registration transform to all these voxels can be very computation demanding. In order to make an efficient and smooth resampling, the binary volume is converted into a triangle mesh before transformation. This approach allows applying the (inverse) registration transform to the liver surface points only and computing the liver voxels only on slices, which intersect the triangle mesh. In addition, the surface-based approach allows smoother interpolation between down-sampled slices (note that the segmentation is performed on a down-sampled image in case of small slice thickness) than the linear, the nearest neighbor, or the spline-based interpolation. Thus, the result is closer to that expected by physicians. Using only the intersection of the arterial and the portal-venous phases, significant over-segmentation can be eliminated at different anatomical regions. These regions are the muscles at the ribcage, the inferior vena cava, the stomach, the pancreas and the bowels (Fig. 10). If some lesions are found in the liver, wherein the intensity is very different from that of liver in one phase, the final result excludes them. In such cases, a more complex function can be used for merging the results (incorporating the original grayscale images, too).

3. Automatic liver segmentation for portal-venous CT images In surgical planning, mostly the portal-venous image is in focus, because the vasculature and the lesions are most visible in this phase. This way a method was developed that works if only the portal image is available. The main challenge of this work was to substitute the multi-phase information with anatomical knowledge. An additional motivation for this development was that in the liver segmentation contest of the 2007 MICCAI workshop (Heimann et al., 2009) only the portal-venous phase was available for liver segmentation.

Fig. 6. Additional segmentation at the right lung. The liver and the lung are segmented (left). The region between the liver and lung surface is determined (center) and regiongrowing with lower intensity statistics is performed in this region (right).

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Fig. 7. Filling the portal vein. Region-growing excluding high intensity vessels (left). As result of the post-processing, these regions (center) as well as other cavities (right) are filled.

Fig. 8. Inter-phase registration problem: axial (left), sagittal (center), and coronal (right) slice of an image belonging to the portal venous phase. The green region represents the segmented liver volume of this phase and the red contour shows the segmentation of another phase. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

clinical exams, which were acquired using different scanners and contrast agents, with different image resolution and quality, the intensity of the liver in the portal-venous phase is always in the range of 50–250 HU. The histogram has two significant maxima in this range. One for the muscles and the other for the liver. The average intensity of the liver maximum is always higher than that of the muscles, and it is always greater than 80 HU. So, the maximum for the liver can be determined robustly. In order to make the liver modus easier to determine, the histogram is calculated for the right half of the image. After the liver modus ðmÞ is calculated the liver intensity range ½l; u is defined as follows. Assume that hðiÞ represents the histogram value at intensity i. The value of l and u is defined as follows:

l

¼ minimal i 2 ½m  50; m such that 0:25  hðmÞ < hðiÞ; and 8 j 2 ½i; m : hðiÞ < hðjÞ

u ¼ maximal i 2 ½m; m þ 50 such that 0:25  hðmÞ < hðiÞ; and 8j 2 ½m; i : hðiÞ < hðjÞ Fig. 9. Inter-phase registration: axial (left), sagittal (center), and coronal (right) slice of an image belonging to the portal-venous phase. The green region represents the segmented liver volume belonging to this phase and the blue contour shows the registered segmentation result belonging to another phase. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 10. Intersection of more phases. The segmentation result belonging to the arterial (left) and the portal-venous (center) phase can be over-segmented, taking the intersection after affine registration can significantly reduce over-segmentation (right).

The workflow of the single-phase method is similar to the multi-phase. Since only the portal phase is available, the initial segmentation was modified, and some post-processing steps were added to reduce over-segmentation. The single-phase method consists of the following main steps. Firstly, an initial segmentation is performed (Section 3.1). Then, the liver is separated from the heart using the method presented in Section 2.2. Starting from the initial region the liver is segmented using the NCRG method presented in Section 2.3. Finally, the post-processing steps of Section 2.4 are performed followed by an additional function that removes inferior vena cava (Section 3.2).

Using the intensity range ½l; u the image is thresholded. In contrast to the multi-phase method the thresholded image involves many voxels outside the liver because of other organs, the intensity of which is similar to that of the liver in the portal-venous phase. In order to remove the non-liver voxels, the image is eroded. Since the liver has large compact volume in the abdomen, a sphere with large diameter (15 mm) can be used as kernel of the erosion without significant loss of liver voxels. Then, the largest connected region is determined. According to our experiences, this region is always located inside the liver. However, its location varies inside the liver, and its volume is less (about 15% of the total liver volume) than in the case of the multi-phase method. Consequently, this initial segmentation is less robust. An example of the initial segmentation for a portal-venous image can be seen in Fig. 11. 3.2. Removing inferior vena cava In case of portal-venous images the intensity of the inferior vena cava (IVC) is very similar to that of the liver parenchyma. Since the

3.1. Initial segmentation for portal-venous CT images In the first step the intensity range of liver voxels is determined on the basis of the histogram. In accordance with a wide range of

Fig. 11. Initial segmentation for portal-venous image. The image is thresholded using the intensity range of the liver (left). Then, the binary image is eroded using a large kernel and the largest 3D connected region is determined (right).

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radius of this vessel is about 10–15 mm, the neighborhood-connected region-growing leaks out through the IVC in nearly 40% of the cases. The idea behind the IVC removal is to detect those parts of the segmented liver, which are similar to a vertical cylinder with the specified diameter. The cross section of the IVC on the axial slices is a circle-shaped region. Such regions can be detected using circular Hough transform. Since the radius of the vessel varies, circles with different radius shall be detected. Thus, instead of a discrete circle a discrete ring is used in the computation of the Hough transform. The inner radius of the ring is smaller (5 mm) and the outer is greater (20 mm) than the average radius of the IVC. Fig. 12 demonstrates detection of circles with variable diameter. Firstly, a probability map is created, that is initially a zero valued image. Then, a discretized binary ring (solid circles) is placed into each contour point (black dot) of the segmented image, and the value of all voxels in the map is increased by one, where both the ring (bright region between the solid rings) and the segmented image (red region) has non-zero value. When all contour points are processed, the probability map has large value at the center of the circle (bright region in the center of the red region), the diameter of which is nearly equal to the average diameter of the IVC (dashed circle). An example of such a probability map can be seen in the center image of Fig. 12. In order to make IVC detection more robust, a ring that is extended in vertical direction (with two slices) is used. The probability map is fist thresholded to reduce the possibility of false detection. The threshold value is equal to 0:3  pmax , where pmax is the largest value of the map and 0.3 is an experimental value. Then, the map is processed slice by slice. All local maxima in each slice are located, and for each maximum, it is checked whether a closed contour is found around it within a local environment as follows. A two-dimensional region-growing is started from the location of the maximum visiting only the non-zero voxels of the segmented image. A maximum is considered to be rounded by closed contour, if the region-growing cannot reach any voxel that is located farther than 20 mm from the starting maximum. If a closed contour is found, the region around the maximum is labeled on the segmented image as candidate for erasing. After processing all the slices, all 3D connected regions are determined, which consist of unlabeled liver voxels. Except for the largest one (that represents the liver), all these regions are labeled as candidate for erasing. Such regions can be found along the IVC, where the vein has a branching point or in the bottom or top of the liver. Fig. 12 demonstrates the result of this step (red regions are labeled as candidates for erasing). Finally each labeled region is erased, their vertical lengths are greater than 25 mm (so that the bottom peaks of the right and left liver lobes are not deleted).

4. Results and discussion In this section the evaluation of the two methods is presented. Section 4.1 describes the qualitative evaluation of the multi-phase

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method using a multi-phase dataset. Section 4.2 describes the quantitative evaluation of the single-phase method using the portal phase MICCAI 2007 training dataset. Section 4.3 presents the quantitative comparison of the two methods using a smaller dataset and demonstrates the advantages of the multi-phase method over the single-phase.

4.1. Qualitative evaluation of the multi-phase method A set of 19 multi-phase examinations (three with 2 phases and 16 with three phases) was used for evaluation of the multi-phase method. Manual segmentation was not available for these images, so the precision was assessed using a questionnaire filled by five radiologists. The test series came from different hospitals and were acquired with different type of scanners according to different acquisition protocols. Fig. 13 demonstrates the result of our method for 3 typical cases. The top row represents a good solution that could be used without manual correction (on the basis of the physicians feedbacks). The middle row shows an acceptable segmentation that can be used after minor corrections. The bottom row shows a bad solution that could be used only after time consuming editing. Tables 1 and 2 summarize the average rating assigned to the segmentation results by the radiologists. According to Table 1, the separation from the chest wall and the heart, the detection of hepatic portal, and the correction of breathing artifacts was successful in more than 82% of the cases. The detection of the caudal part of the left lobe and the bottom part of liver was good in more than 72% of the cases. The detection of the upper part of the liver was successful only in 45% of the cases, but it failed only in 3.75% of the cases. The answer for the last question shows that the result was useful for clinical application in nearly 94% of the cases. The clinical application was liver surgery planning including living related liver transplantation and oncological resection. In the first case, a healthy donor gives a part of his liver to another person. In the latter case, the extent and volume of resection has to be planned. The under- and over-segmentation was also rated by the radiologists. According to Table 2, the average over-segmentation rating was ð3  1:25 þ 4  27:50 þ 5  71:25Þ=100 ¼ 4:7, while the undersegmentation rating was ð1  1:25 þ 2  6:25 þ 3  18:75 þ 4  63:75 þ 5  10:00Þ=100 ¼ 3:75. The latter result was due to large under-segmented lesions, wherein the intensity is significantly different from that of the normal liver in one of the phases. On the basis of these results, the specificity of the method is good because in 71% of the cases the result is not over-segmented and in further 27% the result is imperceptibly over-segmented. The sensitivity of the method is lower because under-segmentation concerns 90% of the cases, although it is acceptable in more than 73%. Our hypothesis about sensitivity and specificity is confirmed by the quantitative analysis of Section 4.3, wherein the results of both methods are compared with manual segmentation.

Fig. 12. The main steps of the IVC removal. Circular hough transform to detect circles on axial slices (left). Probability map representing voxels which are likely to be located inside a vertical tubular structure (center). Detection of large 3D connected tubular structure (right). Red regions are candidate for erasing, the green region (IVC) is erased. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 13. Axial (left), sagittal (middle), and coronal (right) slices of segmentation results rated good (top), acceptable (middle), and bad (bottom) by radiologists.

Table 1 Result of the evaluation questionnaire. The values represent the average of answers made by five radiologists for all 19 cases. Question

Good

Acceptable

Bad

Rate Rate Rate Rate Rate Rate Rate

88.75 86.25 72.50 82.50 73.75 45.00 92.50

11.25 12.50 22.50 13.75 26.25 51.25 7.50

0.00 1.25 5.00 3.75 0.00 3.75 0.00

As it is 55.00

Editing 38.75

Not at all 6.25

separation from chest wall between ribs separation from heart detection of caudal part of the left lobe detection of hepatic portal detection of bottom part of liver detection of upper part of liver correction of breathing artifacts

Question The segmented liver can be used

Table 2 Result of the evaluation questionnaire. The values represent the average of answers made by five radiologists for all 19 cases. The possible answers for the two questions are: (1) the result is useless; (2) the result is largely over/under-segmented, it requires time consuming manual correction; (3) the result is slightly over/undersegmented, it requires medium-time manual correction; (4) the result is imperceptibly over/under-segmented, it requires a little manual correction; (5) the result is not over/under-segmented. Question

1

2

3

4

5

Rate over-segmentation Rate under-segmentation

0.00 1.25

0.00 6.25

1.25 18.75

27.50 63.75

71.25 10.00

4.2. Quantitative evaluation of the single-phase method MICCAI 2007 workshop (Heimann et al., 2009) gave good opportunity to compare recently developed liver segmentation methods. In this contest only the portal-venous phase was used for segmentation. The organizers provided 20 cases (with reference

segmentation) as training set. The preliminary evaluation published in the workshop proceedings (Heimann et al., 2007) was performed using another set of 10 cases (test set), while the onsite competition was performed using a third set of 10 cases (contest set). The reference segmentation of the test and contest sets was not available for the participants, and the contest set was made available at the onsite competition only. Thus, the participants of the onsite competition had to process 10 previously unseen cases. The final ranking2 was determined based on the results of the onsite competition. On the basis of the results of the onsite competition our method (second best in precision) could provide segmentation quality that is comparable with a non-expert manual segmentation for the average clinical case. However, in case of extreme pathology the result was under-segmented (Fig. 14). This is a good result,

2

http://sliver07.isi.uu.nl/miccai.php.

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concerning that it takes about 30 s in average to process one exam, while other methods (including the best one in precision) required about 10 min or more to run. The quantitative evaluation was performed using five difference metrics. Two of them are volume-based, and the other three are surface-based, so the evaluation amplified the surface errors more. The following five metrics were used:  Volumetric overlap error (VOE), in percent:

100   volume of the intersection of segmentation and reference  1 volume of the union of segmentation and reference  Relative volume difference (RVD), in percent:

100 

segmented volume  reference volume reference volume

 Average symmetric surface distance (ASD), in mm: For each contour voxel of the segmented volume the Euclidean distance from the closest contour voxel of the reference volume is computed. This computation is also performed for the contour voxels of the reference volume. The metric represents the average of these distances.  Root mean square symmetric surface distance (RMD), in mm: Similar to ASD, but instead of the Euclidean distance the squared distance is calculated. The metric represents the square root of the average squared distances.  Maximum symmetric surface distance (MSD), in mm. This is as for ASD, but instead of the average, the maximal Euclidean distance is calculated. Table 3 shows the accuracy of the single-phase method for the MICCAI training dataset. On the basis of second column, the relative volume difference (RVD) is 1.7% (absolute RVD 2.9%) in average, which is comparable with the results reported for automated liver segmentation in recent clinical studies (Hermoye et al., 2005; Toshiyuki et al., 2008). The average VOE is 8.2%, and it is above 10% in only two of the 20 cases, when the liver has some large hypodense lesions. In general, the results are under-segmented (negative average value of the RVD), which is mostly due to undersegmented lesions or vessels. The under-segmented parts of the portal vein are mostly responsible for the relatively large surfacebased metrics (ASD, RMD, and MSD). The precision of the algorithm was measured according to a complex scoring system (Heimann et al., 2009), which makes it possible to compare results (Table 3) with those of non-expert manual segmentation. A perfect scoring result (zero for all five metrics) is worth 100 per metric, while the manual segmentation of average quality (6.4%, 4.7%, 1 mm, 1.8 mm, 19 mm) is worth 75 per metric. Note that the absolute value of RVD was taken into account in the scoring system. The score is interpolated between zero and the metric of the average quality and extrapolated above

Fig. 14. Result of the single-phase segmentation for an average (left) and an extreme case (right) of the MICCAI 2007 training dataset.

Table 3 Evaluation of the single-phase method using MICCAI 2007 workshop training dataset and error metrics. Case

VOE

RVD

ASD

RMD

MSD

Score

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

10.8 6.6 8.5 5.9 7.3 7.1 7.8 7.2 8.1 7.7 7.4 7.0 9.7 8.8 4.6 24.2 7.0 5.6 4.6 7.4

4.3 1.6 1.0 0.7 1.2 0.4 2.1 2.9 3.8 0.8 2.7 4.4 0.6 2.6 0.7 21.9 0.5 1.1 0.8 4.4

1.9 1.0 1.4 0.9 1.1 1.0 1.2 1.3 1.3 1.3 1.3 1.1 1.5 1.6 0.7 3.8 1.1 0.9 0.7 1.3

3.8 2.0 2.6 1.5 2.2 1.9 2.4 2.2 2.4 2.5 3.0 2.4 2.4 2.7 1.5 9.0 2.0 2.0 1.3 2.4

31.3 17.7 27.3 19.9 17.1 21.9 21.9 23.5 22.7 20.8 25.4 31.0 15.4 20.3 20.2 55.5 16.4 24.3 13.7 19.8

59 78 71 81 77 78 73 73 70 75 70 70 74 70 83 8 79 78 85 71

8.2 4.1

1.7 5.3

1.3 0.6

2.6 1.6

23.3 8.9

71 16

Average Std. Dev.

Table 4 Scores of the automatic liver segmentation methods based on the evaluation performed at the onsite competition of MICCAI 2007 workshop. Method

Score

Kainuller et al. (2007) Single-phase method Schmidt et al. (2007) Seghers et al. (2007) Saddi et al. (2007) Slagmolen et al. (2007) Furukawa et al. (2007) Susomboon et al. (2007)

68 57 53 51 51 42 42 5

the metric of the average quality, such that negative scores are replaced with zero. According to this calculation, exam 1 (with metrics equal to 10.8%, 4.3%, 1.9 mm, 3.8 mm and 31.3 mm) merits scores of 58, 77, 53, 48, an 59, respectively, so the average score is 59. On the basis of the numbers of the last column, the average precision (71) of our fully automatic method can be claimed nearly as good as the precision of a non-expert manual segmentation (75). In the majority of the cases (90%) the score is greater or equal to 70 and only in two cases has the result low scores (exams 1 and 16). The large VOE in rows 1 and 16 belong to extreme cases (Fig. 14 right), which were under-segmented due to the presence of very large hypodense lesions. That is the most important problem with this as well as other methods (Heimann et al., 2009). As well as the current results available on the Internet3 the result of the onsite competition is worth considering. At this event the groups had to segment 10 previously unseen exams, so they did not have chance to fine-tune their method for the given set of data. In addition, the contest dataset involved relatively more extreme cases than did the training set. Due to the latter reason, the average score of our method for the contest dataset (57) was significantly lower than the value (71) reported in Table 3. In order to compare our method with other automatic methods, the scores achieved at the onsite competition are shown in Table 4. The method of (Kainuller et al., 2007) stands above the others. The 3

http://sliver07.isi.uu.nl/results.php.

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difference is mainly due to the fact, that this method can provide good results even if the liver involves some extremely large lesions. Methods of other groups (Ruskó et al., 2007; Schmidt et al., 2007; Seghers et al., 2007; Saddi et al., 2007) perform nearly at the same quality. These methods can under-segment the large lesions. Methods (Slagmolen et al., 2007; Furukawa et al., 2007) make more mistakes or can fail in some cases and method of Susomboon et al. (2007) fails in most of the cases. From these results it can be claimed that we developed competitive method for automatic liver segmentation.

Table 5 Evaluation of the single-phase method according to the MICCAI workshop and some standard metrics. Exam

VOE

RVD

ASD

RMD

MSD

Score

TPVF

FPVF

1 2 3 4 5

18.56 19.74 20.06 11.07 10.16

0.51 22.51 11.24 5.43 0.61

2.61 5.18 4.79 1.54 1.54

5.66 10.37 12.48 3.48 3.07

48.61 78.93 73.58 34.53 25.21

43 5 13 59 69

90.0 99.1 93.8 96.7 94.9

10.5 23.4 17.4 8.7 5.7

Average Std. Dev.

15.92 4.88

8.06 9.2

3.13 1.75

7.01 4.21

52.17 23.59

38 28

94.9 3.4

13.1 7.2

4.3. Quantitative and qualitative comparison of the two methods Many organs may have similar intensity to that of the liver in a particular phase. This can lead to over-segmentation if only one phase is considered. In some anatomical regions, (stomach, pancreas, bowels, spleen) over-segmentation cannot be handled with simple rules because the location, the size and the shape of the organs varies substantially. In this section, we demonstrate that the multi-phase method provides better result in these regions. The two methods were compared using five representative contrast-enhanced exams (selected from the 19 cases used in the questionnaire-based evaluation). These exams consist of an arterial and a portal-venous phase. The images were segmented by both methods, such that the single-phase method used only the portal-venous image, while the multi-phase used both images. Fig. 15 demonstrates the segmentation result for three cases using different methods. In order to make a quantitative comparison, the liver was manually contoured on the portal-venous image by one expert and the result of both methods was compared with the manual segmentation. Tables 5 and 6 report the precision of the different methods according to the MICCAI and the following two standard metrics:  TPVF = number of true positive voxels in the segmented image/ number of liver voxels in the manual segmentation  FPVF = number of false positive voxels in the segmented image/ number of liver voxels in the manual segmentation The average values in Tables 5 and 6 are better for the multiphase method. One can see that VOE as well as the surface errors are lower for the multi-phase method due to the lack of extensive over-segmentation. Note, that the average VOE is higher than that reported in Table 3. The portal phase of these images has less contrast (Fig. 15) compared to the cases referred in Section 4.2, so the segmentation of the liver volume was more challenging. The average TPVF is reduced by 3.3, which indicates that the multi-phase method is more suited to the normal liver parenchyma. The lower sensitivity of the multi-phase method is also confirmed by the negative average RVD. The 3.3 loss of TPVF is relatively small compared to the much better FPVF that is 9.7 lower for the multi-

phase method. This decrease of FPVF is due to the lack of large over-segmentation. More specifically, subject of exam 1 had a large lesion that was under-segmented by both methods. In this case, the result of the single-phase method was over-segmented in the bowels, which compensated for the loss of volume (identical VOE and different RVD). In case of exams 2 and 3 the single-phase method over-segmented the stomach and some bowels, which increased the MSD and FPVF significantly. For these exams as well as for exams 4 and 5, the multi- phase method provided better results. In order to test the significance of differences, two-tailed paired T-test was performed for each error metric (the bottom row of Table 6). According to the p-values, three metrics (RVD, TPFV, FPVF) show significant difference, RMD shows nearly significant (p = 0.088) difference, while the other differences are not statistically significant. As reported earlier, 7 of 8 metrics has better average value and smaller deviation for the multi-phase method. Since the deviation is still high, the number of cases is too low to demonstrate statistically significant difference for 5 of 8 metrics. However, it is important to note that the largest p-value is 0.177, which implies the multi-phase method is very likely to provide better results than the single-phase. Tables 7 and 8 show the results of the questionnaire-based evaluation (used in Section 4.1) concerning the most important questions. Table 7 demonstrates the opinion of the radiologists about the utility of the result, and Table 8 shows the average rating concerning over- and under-segmentation. According to Table 7 the result of the single-phase method (in contrast with the multiphase) cannot be used without manual editing in any of the five cases. This is due to the fact that the single-phase method oversegments some neighboring organs (Fig. 15). Table 8 shows that the single-phase method has somewhat better rates for under-segmentation but much worse rates for over-segmentation. On the basis of this the overall precision of the multi-phase method is better. In general the multi-phase method provides more reliable result because the possibility of over-segmentation is significantly lower. This is due to the more stable initial segmentation and the intersection of the results belonging to the different phases. Both

Fig. 15. Result of the single-phase (red area) and the multi-phase method (black contour). Over-segmented regions at the pancreas (left – exam 2), the stomach (middle – exam 3), and bowels (right – exam 4) can be reduced by combining more phases. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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L. Ruskó et al. / Medical Image Analysis 13 (2009) 871–882 Table 6 Evaluation of the multi-phase method according to the MICCAI workshop and some standard metrics. Exam

VOE

RVD

ASD

RMD

MSD

Score

TPVF

FPVF

1 2 3 4 5

18.56 9.40 10.36 9.48 9.30

12.65 1.84 5.52 4.10 4.29

2.83 1.68 1.52 1.14 1.18

4.63 2.52 2.38 1.84 2.02

49.41 22.59 21.11 22.26 25.21

32 69 66 72 70

84.1 95.9 91.9 92.9 93.1

3.3 5.9 2.6 2.8 2.6

Average Std. Dev. p-Value

11.42 4.01 0.118

1.70 0.69 0.139

2.68 1.12 0.088

28.12 12 0.129

62 17 0.177

91.6 4.4 0.011*

3.4 1.4 0.024*

*

4.94 5.17 0.009*

Significant difference p < 0:05.

Table 7 Summary of answers for the question ‘‘The segmented liver can be used?”. The values represent the average answers made by five radiologists for the five test cases. Three possible answers are: the liver can be used as is, after a slight manual editing, not at all.

Single-phase method Multi-phase method

As it is

Editing

Not at all

0 80

40 20

60 0

Table 8 Summary of answers for the questions concerning under- and over-segmentation. The values represent the average of answers made by five radiologists for the five test cases. The possible answers were the same as written in the caption of Table 2. 1

2

3

4

5

Single-phase method Rate over-segmentation Rate under-segmentation

36 0

24 0

24 0

16 24

0 76

Multi-phase method Rate over-segmentation Rate under-segmentation

0 0

0 0

4 0

44 64

52 36

methods usually under-segment large (hyper- or hypodense) lesions. These lesions are most visible in the portal-venous phase, so they are under-segmented if this phase is incorporated. The multi-phase method, however, can use any contrast-enhanced phases (unlike the single-phase method that works only for portal-venous phase). It is possible to combine the arterial phase with another less contrasted phase. The under-segmentation of large lesions can be eliminated, if the late phase is used instead of the portal-venous (Fig. 16). In this particular case all three combinations of the three available contrast-enhanced phases were tried and the best result was displayed. The automatic selection of phases, which are the best for segmentation, can be investigated in future work. 5. Conclusion and future work In case of ideally acquired portal-venous images, the singlephase method can provide similar level of precision as the multi-

phase method. In clinical practice, however, the quality of the portal-venous image varies because it depends on several circumstances (acquisition protocol, patient’s condition, contrast agent, etc.). Since the multi-phase method uses more images, it provides more reliable results in those cases, when the liver is not easily separable from the surrounding organs in the portal-venous phase. The single-phase method involves several a pre- and post-processing steps to eliminate over-segmentation at different organs, which can be described with some rules (heart, IVC, vessels). There are some organs, which cannot be described in such an easy way (muscles, organs of digestion). The multi-phase method can efficiently separate these organs from the liver without applying complex rules. From the results demonstrated in Section 4, an automatic tool for liver volume segmentation has been developed, that can be used in clinical practice in most of the cases. In clinical routine, the running time is as important as the precision. Our method satisfies this requirement, too. After optimizations, the fully automatic processing of a contrast-enhanced CT exam (having two phases) takes 25:6 ð7:2Þ seconds in average using Intel Core2 Duo 2.2 GHz CPU with 2 GB RAM. The running time of the single-phase method (without optimization of the additional preand post-processing steps) was 40:7 ð9:4Þ seconds using the same hardware. That is significantly less than the 10–15 min observed at the MICCAI 2007 live contest. The evaluation demonstrated that the overall precision of both methods is good. Both methods provide result that is rarely oversegmented. Under-segmentation, however, must be corrected. Special attention is needed for cases, when large lesions (10–25% of the total volume) can be found in the liver. According to the statistics reported about the size distribution of liver lesions (van Leeuwen et al., 1996), our methods can be used for most of the clinical cases. This problem can be solved by using a more complex function for combining different phases or by detecting lesions in the environment of the healthy liver parenchyma based on their characteristic contrast uptake. Eliminating under-segmentation of large lesions can be also solved by incorporating shape or probabilistic model in the segmentation. According to the results presented in the MICCAI 2007 workshop proceedings (Heimann

Fig. 16. Advantage of the multi-phase method. Use of the portal-venous phase only (left, single-phase method) or combined with the arterial phase (center, multi-phase method) may result in under-segmented image. When the arterial image is combined with another less contrasted phase, the multi-phase method provides better result (right).

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et al., 2007), methods (Kainuller et al., 2007; Chi et al., 2007; van Rikxoort et al., 2007) can solve this problem. The running time of these methods (10–15 min in average), however, can limit their utility in some clinical applications. Acknowledgements Some components of the presented multi-phase method were developed in cooperation of the University of Szeged and GE Hungary Healthcare Division. Hereby, we would like to thank all members of the university team lead by Krisztina Dóra, namely Norbert Bara, Csaba Domokos, Tamás Kórodi, László Reszegi, Árpád Tigyi, and Norbert Zsótér. Furthermore, we are very grateful to the medical evaluation team lead by Prof. András Palkó Ph.D, namely Katalin Gion M.D., Edit Kukla M.D, and Endre Szabó M.D. for their very important clinical feedbacks. We also emphasize the contribution of our colleague, Eric Pichon Ph.D, who helped with collecting multi-phase contrast-enhanced CT exams from various clinical sites. References Bekes, Gy., Nyúl, L.G., Máté, E., Kuba, A., Fidrich, M., 2007. 3D segmentation of liver, kidneys and spleen from CT images. Proceeding of the International Journal of Computer Assisted Radiology and Surgery 2 (1), 45–46. Chi, Y., Cashman, P.M.M., Bello, F., Kitney, R.I., 2007. A discussion on the evaluation of a new automatic liver volume segmentation method for specified CT image datasets. In: MICCAI 2007 Workshop Proceedings of the 3D Segmentation in the Clinic: a Grand Challenge, pp. 167–178. Duda, D., Kretowsky, M., Bezy-Wendling, J., 2006. Texture characterization for hepatic tumor recognition in multiphase CT. Biocybernetics and Biomedical Engineering 26 4, 15–24. Furukawa Daisuke, Shimizu Akinobu, Kobatake Hidefumi, 2007. Automatic liver segmentation method based on maximum a posterior probability estimation and level set method. In: MICCAI 2007 Workshop Proceedings of the 3D Segmentation in the Clinic: a Grand Challenge, pp. 117–124. Heimann, T., Wolf, I., Meinzer, H.P., 2006. Active shape models for a fully automated 3d segmentation of the liver – an evaluation on clinical data. In: Larsen, R., Nielsen, M., Sporring, J. (Eds.), MICCAI 4191 of LNCS. Springer-Verlag, pp. 41–48. Heimann, T., van Ginneken, B., Styner, M., 2007. 3D segmentation in the clinic: a grand challenge. In: MICCAI 2007 Workshop Proceedings of the 3D Segmentation in the Clinic: a Grand Challenge, pp. 7–15. . Heimann, T., van Ginneken, B., Styner, M., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, Gy., Bello, F., Binnig, Bischof, H., Bornik, A., Cashman, P.M.M., Chi, Y., Córdova, A., Dawant, B.M., Fidrich, M., Furst, J., Furukawa, D., Grenacher, L., Hornegger, J., Kainmuller, D., Kitney, R.I., Kobatake, H., Lamecker, H., Lange, T., Lee, J., Lennon, B., Li, R., Li, S., Meinzer, H., Németh, G., Raicu, D.S., Rau, A., van Rikxoort, E., Rousson, M., Ruskó, L., Saddi, K.A., Schmidt, G., Seghers, D., Shimizu, A., Slagmolen, P., Sorantin, E., Soza, G., Susomboon, R., Waite, J.M., Wimmer, A., Wolf, I., 2009. Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Transactions on Medical Imaging 28 (8), 1251–1265. Hermoye, Laurent, Laamari-Azjal, Ismael, Cao, Zhujiang, Annet, Laurence, Lerut, Jan, Dawant, Benoit M., Van Beers, Bernard E., 2005. Liver Segmentation in Living Liver Transplant Donors: Comparison of Semiautomaticand Manual Methods. Radiology 234, 171–178. Kainuller, D., Lange, T., Lamecker, H., 2007. Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: MICCAI 2007 Workshop Proceedings of the 3D Segmentation in the Clinic: a Grand Challenge, pp. 109–116. Lamecker, H., Lange, T., Seebass, M., 2004. Segmentation of the liver using a 3D statistical shape model. ZIB-Report 04-09 Konrad-Zuse-Zentrum fur Informationstechnik, Berlin. Nakayama, Yoshiharu, Li, Qiang, Katsuragawa, Shigehiko, Ikeda, Ryuji, Hiai, Yasuhiro, Awai, Kazuo, Kusunoki, Shinichiro, Yamashita, Yasuyuki, Okajima, Hideaki, Inomata, Yukihiro, Doi, Kunio, 2006. Automated hepatic volumetry for

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