Ischemic stroke lesion segmentation using stacked sparse autoencoder

Accepted Manuscript Ischemic stroke lesion segmentation using stacked sparse autoencoder G.B. Praveen, Anita Agrawal, Ponraj Sundaram, Sanjay Sardesai PII:

S0010-4825(18)30140-9

DOI:

10.1016/j.compbiomed.2018.05.027

Reference:

CBM 2978

To appear in:

Computers in Biology and Medicine

Received Date: 7 February 2018 Revised Date:

23 May 2018

Accepted Date: 29 May 2018

Please cite this article as: G.B. Praveen, A. Agrawal, P. Sundaram, S. Sardesai, Ischemic stroke lesion segmentation using stacked sparse autoencoder, Computers in Biology and Medicine (2018), doi: 10.1016/j.compbiomed.2018.05.027. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

FLAIR

ACCEPTED MANUSCRIPT

W(1)

x1

h1(1)

W(2)

h1(2)

x2 h2(1)

h2(2)

bx Ground truth

Input Layer

(1)

TE D

h sl

M AN U

. . bh(1)

. .

h sl

(2)

bh(2)

. . h sl

SVM Classifier

( 3)

bh(3)

EP

xn

h3(1)

(4) h1(3) W

AC C

T2

. . .

SC

x3 T1

W(3)

RI PT

DWI

1st Hidden Layer

2nd Hidden Layer

3rd Hidden Layer

Output Layer

ACCEPTED MANUSCRIPT

RI PT

Ischemic stroke lesion segmentation using stacked sparse autoencoder Praveen G.Ba,∗, Anita Agrawala , Ponraj Sundaramb , Sanjay Sardesaic

Department of Electrical and Electronics Engineering, BITS PILANI - K.K Birla Goa campus, Goa, India b Department of Neurosurgery, Goa Medical College, Goa, India c Department of Radiodiagnosis, Goa Medical College, Goa, India

M AN U

SC

a

Abstract

AC C

EP

TE

D

Automatic segmentation of ischemic stroke lesion volumes from multi-spectral Magnetic Resonance Imaging (MRI) sequences plays a vital role in quantifying and locating the lesion region. Most existing methods mainly rely on designing hand-crafted features followed by a classifier model for ischemic stroke lesion segmentation. Design of these features requires complex domain knowledge and often lacks the ability to differentiate between the stroke lesions and the normal classes. In this work, we propose an unsupervised featured learning approach based on stacked sparse autoencoder (SSAE) framework for automatically learning the features for accurate segmentation of stroke lesions from brain MR images. A deep architecture is designed using sparse auto-encoder (SAE) layers, followed by support vector machine (SVM) classifier for classifying the patches into normal or lesions. We validated our approach on a publicly available Ischemic Stroke Lesion Segmentation (ISLES) 2015 dataset, with a mean precision of 0.968, mean dice’s coefficient (DC) of 0.943, mean recall of 0.924 and mean accuracy of 0.904. The experimental results show that our proposed approach significantly outperforms the state-of-the-art methods in terms of precision, DC, and recall. Quantitative evaluation was carried out and compared with the existing approaches, which demonstrates that the proposed method is 25.71%, 36.67%, and 16.96% higher in terms of precision, DC and recall values, respectively. The unsupervised features learned via SSAE framework performs better than ∗

Corresponding author Email address: [email protected] (Praveen G.B)

Preprint submitted to Computers in biology and medicine

May 31, 2018

ACCEPTED MANUSCRIPT

RI PT

the hand-crafted features and can be easily trained on large datasets. Keywords: Ischemic stroke lesion segmentation, Magnetic Resonance Imaging, stacked sparse autoencoders, unsupervised feature learning, SVM. 1. Introduction

AC C

EP

TE

D

M AN U

SC

Stroke is the third leading cause of death and a major cause of disability in the developed countries, affecting one in six adults, with an estimated 3 - 6 million cases of stroke annually [1]. Stroke survivors have an increased risk of spontaneous seizures, with stroke being the major cause of acquired epilepsy in adults [2]. Stroke is classified into ischemic (blockage in a blood vessel) stroke or a hemorrhagic (rupture of a blood vessel) stroke. Ischemic stroke constitutes about 80 - 85 % of all the strokes [3, 4]. Age, hypertension, gender, ethnicity and physical inactivity are some of the major risk factors causing ischemic stroke [5]. An ischemic stroke if not treated in time can paralyze one side of the human body and finally can lead to the death of the individual. Feigin et al. [6] analyzed stroke incidence and case fatality studies for a duration of four decades (1970 - 2008). Two country income groups (high-income countries and low to middle-income countries) were considered in this study. The stroke incidence study was performed based on the stroke pathological types such as ischemic stroke, primary intra-cerebral hemorrhage, and subarachnoid hemorrhage. The report demonstrated that the stroke incidence was decreased by 42% in high-income countries, whereas 100% increase in stroke incidence was reported in low to middle-income countries. Imaging modalities such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are widely used in brain stroke imaging. MRI sequences such as T1 weighted, T2 weighted, Fluid-attenuated inversion recovery (FLAIR) and Diffusion-weighted Imaging (DWI) sequences are acquired at different time instances to detect the stroke lesions in the brain. CT offers wider availability, lower cost, effectiveness and sensitivity to detect early stages of stroke. A hemorrhagic stroke appear as a bright region, whereas the ischemic stroke appears dark in CT images. The initial signs of ischemic stroke appear as a hypodense region which is hard to be identified by CT. Chalela et al. [7] demonstrated that MRI is better than CT for detecting acute ischemic stroke and chronic hemorrhagic stroke. Hence MRI is the preferred modality to evaluate the presence, location and evolution of ischemic 2

ACCEPTED MANUSCRIPT

AC C

EP

TE

D

M AN U

SC

RI PT

stroke lesions. Barber et al. [8] proved that DWI sequences can detect the early stages of ischemic stroke. Structural and functional brain mapping signifies the regions involved in a given process, and this is performed by a neuroscientific approach known as lesion-symptom mapping [9]. Lesion-symptom mapping involves manual segmentation of stroke lesions in several MR volumes which is time-consuming and prone to high inter and intra-observer variability. Hence the development of an automatic stroke lesion segmentation method is required to quantify the stroke lesions over time and enable fully automatic screening of acquired scans. Several methods have been presented in the past to detect stroke lesions using manual, semi-automated and automated techniques. Rekik et al. [10] assessed various semi-automatic and fully automatic 2D/3D medical image analysis methods and mathematical models applied for infarct segmentation, ischemic tissue prediction and dynamic simulation of lesion core. Oskar Maier et al. [11] evaluated and compared ischemic stroke lesion segmentation accuracy and reliability using nine different classification methodologies on Ischemic Stroke Lesion Segmentation (ISLES) 2015 dataset. The results proved that a random decision forest and a convolutional neural networks (CNN) classification approach performed better in terms of overall segmentation accuracy compared to the remaining seven classification methodologies. Texture-based feature extraction plays a vital role in brain stroke lesion segmentation [12]. Textural features such as local intensity and energy distribution of ROI, texture pattern, directionality, emerging shapes and smoothness of extracted edges were extracted and analyzed in source and image decomposed domains for stroke recognition [13]. Neethu et al. [14] used wiener, median filtering and discrete wavelet decomposition based preprocessing methodologies to remove noise from the CT images followed by Gabor filtering approach to extract the edges and contours of the images and segmentation of stroke region was performed with the help of region growing [15]. Another type of textural feature extraction technique called as the Gray Level Co-occurrence matrix (GLCM) was used to extract features from the input images [16, 17], followed by neural network model for classification purpose [18, 19]. Textural based feature extraction and normalized graph cut based segmentation technique was proposed for detecting and segmenting ischemic stroke from MR images [20]. Karthikeyan et al. [21] used a modified region growing approach to segment stroke lesions followed by the probabilis3

ACCEPTED MANUSCRIPT

AC C

EP

TE

D

M AN U

SC

RI PT

tic neural network to classify segmented region into stroke lesion class or normal class. The improved level set approach was proposed by optimizing the zero-level set initialization, where brain density analysis determines zero level set [22]. Fusion of CT and DWI-MRI image was carried out by Praveen et al. [23] to obtain better information than using just a single modality. An appearance-based linear dimensionality reduction algorithm such as locality preserving projections (LPP) has been used to fuse CT and MR images, followed by stroke lesion segmentation using K-means clustering algorithm [24]. Karthik et al. [25] proposed a discrete curvelet transform methodology based on multidirectional features for characterizing the brain tissues. An automated segmentation framework for ischemic stroke lesion segmentation in multi-spectral MRI images using random forest(RF) was proposed [26, 27, 28]. Wang et al. [29] proposed a fully automatic machine learning based stroke lesion 3D segmentation approach using RF classifier . Joseph Griffis et al. [30] proposed an automated stroke lesion detection methodology in individual T1-weighted MRI scans using naive Bayes classifier. A supervised method based on cascaded extremely randomized forest classifiers for stroke lesion segmentation has been proposed [31]. The cascaded approach is used to increase the computational efficiency and spatial consistency. Halme et al. [32] proposed a methodology which combines RF classifier and subsequent contextual clustering for ischemic stroke lesion segmentation. An automatic method for sub-acute ischemic lesion segmentation in MR images has been proposed [33]. Voxel-wise classification was performed by extra tree forest framework and intensity based feature extraction techniques were employed to extract features. Goetz et al. [34] proposed a methodology which adaptively trains a new classifier for every new image instead of using a single classifier to train the complete dataset. Neighborhood Approximating Forests (NAF) classifier was used to estimate the dice score for new images with unknown voxel labels. Manual lesion segmentation is a tedious process, prone to intra or interobserver variability and is time-consuming. A semi-automated lesion segmentation approach called as the Clusterize algorithm has been proposed [35]. The results proved that the proposed method significantly decreased the segmentation time without comprising on the precision and reproducibility factors. A semi-supervised technique using Generative Adversarial Network (GAN) for segmentation of brain lesion from MR images has been proposed [36]. Unsupervised feature learning based denoising autoencoder model was used for 4

ACCEPTED MANUSCRIPT

AC C

EP

TE

D

M AN U

SC

RI PT

brain lesion detection, segmentation, and false positive reduction [37]. A single layer denoising autoencoder was used with a kernel size of 21×21 drawn from all four sequences of MR images. Yeu-Sheng Tyan et al. [38] proposed an ischemic stroke detection approach based on unsupervised feature perception enhancement method. Deep Neural Networks (DNNs) are often successful at solving problems for which useful high-level features are not evident to design. Liang Chen et al. [39] proposed two-CNN based approach to automatically segment stroke lesions in DWI. CNN models have been widely used for ischemic stroke lesion segmentation. Dutil et al. [40] proposed a two-pathway framework where each pathway learns about local image details or large context of tissue appearance. Concatenation of separate channels feature maps is performed just before the output layer. A fully convolutional layer is stacked on the top to predict class labels using softmax classifier. Post-processing is performed to remove the outliers and flat blobs which may hinder the prediction accuracy. kamnitsas et al. [41] developed a 11-layers deep, double-pathway, 3D CNN model for brain lesion segmentation. Two deep CNN models inspired by fully CNN and U-Net model are amalgamated to extract millions of 3D patches and 3D convolutional kernels [42]. A fully automatic approach for lesion segmentation using 3D CNN has been proposed [43]. Shuihua Wang et al. [44] proposed a CNN model using rank based average pooling approach to detect cerebral micro-bleeds (CMB) in susceptibility weighted imaging (SWI). Handcrafted feature design involves finding the right trade-off between accuracy and computational efficiency. Handcrafted features combined with CNN model for ischemic stroke lesion segmentation has been proposed by Shen et al. [45]. CNN learns discriminative local features and yield better performance than handcrafted features. In this paper, we address the automatic segmentation of sub-acute ischemic stroke lesion segmentation using stacked sparse autoencoder (SSAE) framework. SAE layers are stacked one upon another in a hierarchical manner to obtain SSAE architecture with support vector machine (SVM) classifier as the output layer. A patch based approach is used to extract positive and negative classes from the input MRI. A SVM classifier will output labels and scores for the test image. The classifier output labels signify to which class the patch belongs to and the scores represent the probability of a pixel belonging to foreground or background. Reconstructing the probability map helps in predicting the accuracy of the classifier model, the detected ischemic stroke lesion is segmented by using an optimal threshold obtained by Receiver 5

Operating Characteristic (ROC) curve. The major contributions of this paper are:

RI PT

ACCEPTED MANUSCRIPT

1. We propose a patch-based approach to tackle the class imbalance problem by efficiently sampling an equal number of patches from both normal and stroke lesion classes.

M AN U

SC

2. We propose an unsupervised feature learning module based on stacked sparse autoencoder for sub-acute ischemic stroke lesion segmentation. SSAE based framework learns high-level features from a large number of unlabeled image patches which automatically learns the most discriminative set of features - that underscores the interclass differences (between the lesion and normal class) to be large, while keeping intraclass differences (between lesion class) to be small. This results in the accurate segmentation of ischemic stroke lesions in brain MR volumes.

TE

D

3. We design a five-layer SSAE architecture by training a SVM classifier in a supervised manner. Each image patch to be classified is fed into the SSAE model, which extracts features and classifies the image patch into ischemic stroke lesion or normal class. The proposed approach is validated on the publicly available ISLES 2015 dataset and the results demonstrate that the proposed approach performed better than the state-of-the-art approaches.

AC C

EP

The remainder of the paper is organized as follows. Section 2 presents the details of the various phases involved in the proposed approach, namely preprocessing, SSAE framework, classification, and post-processing. The performance evaluation of the proposed approach is presented in section 3. Key findings are discussed in section 4 followed by concluding remarks of the proposed work drawn in section 5. 2. Materials and methods 2.1. Materials The input MR sequences are obtained from the ISLES 2015 challenge dataset [46]. The challenge has two subtasks: sub-acute ischemic stroke lesion segmentation (SISS) and acute stroke outcome/penumbra estimation (SPES). In this work, we consider sub-acute ischemic stroke lesion segmentation. The SISS training dataset consists of 28 brain MR volumetric scans 6

ACCEPTED MANUSCRIPT

DWI

T1

T2

Ground truth

SC

RI PT

FLAIR

Figure 1: Sample MRI sequences.

TE

D

M AN U

with four sequences namely: T1, FLAIR, DWI, and T2. Ischemic stroke lesion appears dark in T1 weighted sequence while it appears brighter in a T2 weighted sequence. DWI sequence is extensively used in the diagnosis of stroke, as early stages of stroke lesions can be detected in DWI sequence. Each volume contains 153 to 154 MR slices, with each slice having a resolution of 230 × 230 pixels. The format of the input MRI volumes are in uncompressed Neuroimaging Informatics Technology Initiative (NIfTI) format. All volumes are co-registered to the FLAIR sequences, skull-stripped and resampled to an isotropic spacing of 13 mm. The proposed experiments are carried out with axial slices. Fig. 1 demonstrates the input modality sequences used in the proposed framework.

AC C

EP

2.2. Proposed Method The flow chart of the proposed system is illustrated in Fig. 2. Development of an efficient method for ischemic stroke segmentation will assist radiologist for better diagnosis. MR volumetric scans are considered as input data. The proposed methodology has four stages, viz: preprocessing, patch extraction, SSAE framework, and classification. Since MRI scans typically have varied intensity ranges and are affected by bias fields differently, we performed bias field correction using N4ITK [47] technique followed by intensity normalization. Extraction of patches from all four modalities: T1, T2, FLAIR and DWI sequences is performed from the preprocessed MR volumes. The patches extracted are concatenated to form an input vector, which is then fed into the SSAE framework to obtain features in an unsupervised manner. These features are trained using a SVM classifier to predict labels of an unknown patch from the test image. The proposed method predicts the class label of a pixel by processing the ‘p × p’ patch centered on a pixel of 7

ACCEPTED MANUSCRIPT

Ischemic stroke lesion patches

Unsupervised Feature learning

Stacked Sparse Autoencoders

Pre-processing Normal class patches

SC

Input MR volumes

RI PT

Patch extraction

Detected ischemic stroke lesions

Support Vector Machines

M AN U

Figure 2: An overview of the proposed ischemic stroke lesion segmentation approach.

interest. The input to the proposed SSAE framework is a ‘p × p’ (2D) patch with different sequences including T1, T2, FLAIR, and DWI.

AC C

EP

TE

D

2.2.1. Pre−processing Intensity inhomogeneities in MR images can substantially reduce the accuracy of segmentation. N4ITK [47] bias field correction technique is applied to MR images to make the intensity constant throughout the image, as shown in Fig. 3. Mean intensity values and standard deviation across all the training patches are computed. Patch normalization is performed to have zero mean and unit variance [48]. Whitening is used in the patch pre-processing stage after normalization to remove redundancy in the input by making the adjacent pixels less correlated [49].

(a)

(b)

Figure 3: Pre-processing. (a) Input MRI slice; (b) Pre-processed MRI slice.

8

ACCEPTED MANUSCRIPT

SC

RI PT

2.2.2. Patch extraction Patches are extracted from both training and testing data along with the corresponding ground truth. The size of each patch is chosen to be 11 × 11 = 121 pixels, which is often enough to contain an ischemic stroke lesion region within the desired patch size. The stride length of 3 pixels is used during training to avoid overlapping of the patches, while it is not considered during the testing phase.

AC C

EP

TE

D

M AN U

2.2.3. Stacked Sparse Auto Encoders An autoencoder is an artificial neural network used to learn high-level features in an unsupervised manner. Stacking layers of autoencoders one upon another form a hierarchical deep autoencoder model. Autoencoders compress the input into a latent space representation and then reconstruct the output from the latent space representation. They play a fundamental role in deep architectures for transfer learning, unsupervised feature learning, and other tasks [50]. They are mainly used for dimensionality reduction and data compression tasks. An autoencoder tries to function Hw,b ≈ x  learn the m and ‘x’ represents the dataset x1 , x2 , ...., xm , xi ∈ ℜ . It contains two blocks: an encoder and a decoder. The encoder maps the input to the hidden nodes, whereas the decoder maps the hidden nodes back to the original input. Single layer autoencoder is a neural network which is composed of three layers: an input layer, a hidden layer and an output layer as shown in Fig. l 4. Where Wi,j is the weighted connection between unit j of layer l and unit i of layer l + 1, bli is the bias associated with unit i in layer l + 1. W 1 matrix is composed of weighted connection between input data and hidden units. W 2 is the weighted connection between hidden units and an output layer. bx is the bias from the input layer to each hidden layer and b1 represents bias from bias unit in hidden layer to output layer. Hidden layer is used for encoding the data, and the decoding is done at the output layer. Each single layer module has a set of parameter (W, b) = W 1 , bx , W 2 , b1 which represents the weights and biases from input units to the hidden layer and from hidden layer to output units as shown in Fig. 4. The output activation vector from the first autoencoder is used as an input to the second autoencoder. Consider single training example (x, y), the cost function is defined as J(W, b; x, y) =

1 khW,b(x) − yk2 2

where J(W, b; x, y) is the mean square error (MSE) cost function. 9

(1)

W (1)

W ( 2)

X1

ˆ3 X

b (1)

(x)

SC

ˆ2 X

h (21 )

X3

Hidden layer

M AN U

b

ˆ1 X

h 1( 1 )

X2

RI PT

ACCEPTED MANUSCRIPT

Input

Output

Figure 4: Basic framework of an Autoencoder model.

D

Consider training set of ‘N’ examples, the overall cost function is defined as: " # sl−1 sl nl X N X (l) 1 X λX J(W, b; x(i), y(i)) + (2) E= (Wij )2 N i=1 2 l=1 i j

EP

TE

Substituting Eq. 1 in Eq. 2 " # sl−1 sl nl X N  X (l) 1 X 1 λX 2 E= khW,b (x(i)) − y(i)k + (Wij )2 N i=1 2 2 l=1 i j

(3)

AC C

The first term in the Eq. 3 is the MSE term and the second term in the Eq. 3 is a weight decay or a regularization term which is used to prevent over-fitting of the data by decreasing the magnitude of weights. The autoencoder network is trained to reconstruct the given input, which forces the hidden layer to learn good representations of the inputs and minimize the reconstruction error between the given input and the reconstructed data by using backpropagation algorithm. MSE is used to measure the reconstruction error between the input and the reconstructed output. Low values of reconstruction error determine the reconstruction accuracy of an autoencoder module. Minimization of MSE is performed by initializing the weights and bias in each layer to a small value near zero followed by optimizing the cost function using gradient descent (GD) algorithm. Backpropagation algorithm is used to calculate the partial derivatives in GD followed by the computation of an activation vector. 10

ACCEPTED MANUSCRIPT

SC

RI PT

Sparse autoencoder is an unsupervised feature learning algorithm, where sparsity is imposed onto the hidden units during training. Sparse representation of inputs is useful in pretraining for classification problems. Sparsity can be achieved by adding extra terms in the loss function during training [51] or by replacing few values of the strongest hidden unit activations by zero [52]. Thus the overall cost function of a sparse autoencoder is " # sl−1 sl nl X sl N  X X (l) λX 1 X 1 2 2 E= khW,b (x(i)) − y(i)k + (Wij ) +β KL (ρ k ρbi ) N i=1 2 2 l=1 i j i=1

AC C

EP

TE

D

M AN U

(4) 1−ρ where KL (ρ k ρbi ) = ρlog ρbρi + (1 − ρ)log 1− . ρbi KL is called as the Kullback-Leibler (KL) [53] divergence between the averaged activation of hidden unit ‘i’ and desired activations ‘ρ’. Euclidean distance between H(W,b) and y(i) is represented by khW,b (x(i)) − y(i)k. An autoencoder with multiple hidden layers is termed as a deep autoencoder. Training these deep autoencoders is performed by pretraining the stack of a single layer of autoencoders. Once pretraining is completed, training the entire deep autoencoder is performed by fine-tuning all parameters together. Bengio et al. [54] demonstrated that deep neural networks performance is better than that of shallow neural networks. (1) (1) (1) The hidden layer activations h1 ,h2 , . . . ,hs shown in Fig. 5 are the (2) (2) (2) first layer features, whereas h1 ,h2 , . . . ,hs are the second layer features, (3) (3) and the final layer features denoted by h1 , . . . ,hs are extracted by the autoencoder in layer-by-layer manner. First order features such as edges in the images are obtained at the first layer of SAE, whereas the second layer of SAE learns second-order features that correspond to the patterns in the first order features. Higher layers of SAE learns the higher order features by efficiently representing the data. SSAE model is a deep network model which hierarchically learns the features using multiple levels of abstraction. SSAE stacks multiple SAEs on top of each other with SVM classifier at the output layer for building deep layered architecture. Fig. 5 illustrates the proposed SSAE model with ‘W ’ representing the weights, ‘b’ representing bias of the corresponding three-layered SAE network and ‘h’ denoting the hidden layers. In this work, we design a five-layer SSAE architecture, first layer is the input layer followed by three SAE layers and an output layer. Each hidden layer contains 120, 80 and 50 neurons. L2-weight regularization (λ) is initialized to a value of 0.0002, sparsity regularization (β) to 1.6 and spar11

ACCEPTED MANUSCRIPT

W(1)

h1(1)

x1

W(2)

h1(2)

DWI

h2(1) h3(1)

. . h sl

xn

h sl

(1)

h sl

(2)

bh(2)

bh(1)

M AN U

T2

. .

. .

bx Ground truth

1st Hidden Layer

Input Layer

SVM Classifier

( 3)

SC

h2(2)

x3

. . .

W(3)

(4) h1(3) W

x2

T1

RI PT

FLAIR

2nd Hidden Layer

bh(3)

3rd Hidden Layer

Output Layer

D

Figure 5: An illustration of Stacked Sparse Autoencoder architecture.

TE

sity proportion (ρ) to 0.3. The logistic sigmoid activation function used for encoding and decoding is defined as follows

EP

f (z) =

1 1 + exp−z

(5)

AC C

The input layer consists of patch size 11 × 11 = 121 pixels, where these patches are fed into the first hidden layer, which yields a 120 dimension feature vector representing the edges present in the input image patch. These features form an input to the 2nd hidden layer of size 80 neurons, which in turn outputs 80 dimensional features that depicts the contours of image patches. These second hidden layer features are fed into the third hidden layer of size 50 neurons. SVM, a supervised classifier is used at the output layer for discriminating between the ischemic stroke lesion and normal classes. The SVM classifier can handle high dimensionality data more efficiently and is robust against noisy and outliers samples. The high-level features obtained from the last layer of SSAE are fed as an input to the SVM classifier, which produces the probability of the test patch belonging to class labeled ‘1’(ischemic stroke lesion) or class labeled ‘0’ (normal). 12

ACCEPTED MANUSCRIPT

Symbol X

Description Set of entire training patches

Y

Label set of training patches

x(i)

Intensity vector for ith patch

y(i)

Label of the ith patch

RI PT

Table 1: Symbols used in this paper.

Number of entire training patches

nl

Number of layers in network, Layer ‘L1 ’ is the input layer, ‘L2 ’, ‘L3 ’, ‘L4 ’ are the three hidden layers and ‘L5 ’ is the output layer

SC

N

Hidden units in layer ‘l’

Number of neurons in layer ‘l’

M AN U

sl hls h1s

Number of neurons in the first hidden layer

h2s

Number of neurons in the second hidden layer

h3s

Number of neurons in the third hidden layer

λ

Coefficient for the L2-weight regularization term

β

Coefficient for the sparsity regularization

ρ

Sparsity Proportion

f (z)

Encoder/Decoder transfer function

Bias term associated with unit ‘i’ in layer ‘l+1’

Wl

Weights term associated with layer ‘l’

D

bli

EP

TE

2.2.4. Post-processing A post-processing stage is performed to remove the outliers and flat blobs based on the connected component analysis. An optimal threshold which is obtained from the ROC curve is used to segment the ischemic stroke lesions from the posterior probability image.

AC C

3. Experimental results The simulations are carried out on a 3.40 GHz CPU with four cores and 32GB RAM using a MATLAB environment. SISS MR volumes are selected from the dataset, slice by slice operation is carried out, and each slice is resized to a standard size of 230 × 230. Non-overlapping patches of size 11 × 11 are extracted, so that it contains ischemic stroke lesions. A manually segmented ground truth depicting the presence or absence of stroke lesions is provided in [46]. Patches containing lesions are labeled as ‘1’, whereas patches that do not contain any lesions are labeled as ‘0’. The final training set consists of 6,81,260 patches of stroke lesions, and 6,81,260 patches of normal class, which is then used to train the SSAE and SVM classifier. In 13

ACCEPTED MANUSCRIPT

(a)

TE

D

M AN U

SC

RI PT

this work, we perform a leave-one-out cross-validation (LOOCV) scheme to evaluate the performance of the proposed approach. In this scheme, a one volume is used for testing, whereas the remaining 27 volumes are employed for training in each round. The test image is fed into the SSAE model for ischemic stroke lesion detection. The SVM classifier outputs a score that represents the posterior probability, which depicts the probability of a given pixel belonging to the ischemic stroke lesion or normal class. On reconstructing the scores vector, a probability image is obtained. An optimal threshold which is obtained from the ROC curve is used to segment the ischemic stroke lesions from the posterior probability image. The probability map for a sample test image is shown in Fig. 6.

(c)

(b)

(d)

EP

Figure 6: Ischemic stroke lesion segmentation using Stacked Sparse Auto-encoder Model. a) Test Image; b) Ground-truth; c) Probability map of SVM classifier; d) Post processed ischemic stroke lesion segmented region.

AC C

3.1. Parameter setting SSAE framework has been tested with a range of values for each regularization parameter to minimize the cost function in Eq. 4. The values of all the parameters shown in Table 2 are as follows: h1s =120, h2s =80, h3s =50, λ = 0.0002, β =1.6, ρ =0.3, patch size of 11 × 11 and stride length = 3. Weights of the network are randomly initialized and the training is performed using gradient descent algorithm. An equal number of normal and stroke lesion patches are included in the input layer dataset of size 13, 62, 520 × 121. The logistic sigmoid transfer function is used in the encoder and decoder network. Trained SVM classifier yields an output consisting of labels and scores. Label ‘0’ denotes normal class, and label ‘1’ denotes ischemic stroke lesion class. 14

ACCEPTED MANUSCRIPT

Parameter Value

nl 5

h1s h2s 120 80

RI PT

Table 2: Parameter setting.

h3s λ β 50 0.0002 1.6

ρ 0.3

EP

TE

D

M AN U

SC

The selection of patch size is based on the highest value of dice’s coefficient (DC), accuracy and recall values obtained as shown in Fig. 7. From Fig. 7 (a), (b) and (d), we observe that highest values are obtained at 11 × 11 patch size when compared to the other patch dimensions. Whereas, in Fig. 7 (c) the precision value corresponding to the patch size of 11 × 11 is smaller than that of 9 × 9. In addition the ischemic stroke lesions are not distinguishable in 9 × 9 patches when compared to the 11 × 11. Therefore, we empirically choose an input patch size of 11 × 11 in our work. The hyper-parameters such as β, λ, and ρ are selected based on the minimization of the discrepancy between the input and its reconstruction. This discrepancy is measured in terms of MSE. On computing each hyperparameter, the other two hyper-parameters are kept constant and the corresponding values are computed. Then the permutations values of the individual hyper-parameters are calculated as shown in Fig. 8. The gradual decrease of MSE values leading to its saturation with respect to the numbers of epochs is depicted in Fig. 9. From this argument, we find the least value of MSE at the 1000th epoch, which provides the best reconstructed input. The hyper-parameters corresponding to this least value is considered in our work.

AC C

3.2. Quantitative analysis Evaluation metrics such as DC, accuracy, recall, precision, specificity and area under the receiver operating characteristic curve (AUC) are used to compare the performance of the proposed approach with existing state-ofthe-art techniques. These metrics are obtained based on the number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN). Sensitivity refers to the classifier ability to correctly detect ischemic stroke lesion, whereas specificity computes the proportion of negatives which are correctly identified. The ROC curve is formed by plotting the true positive rate (sensitivity) against false positive rate (specificity) at different threshold limits. The area under ROC curve indicates the classifiers probability to classify ischemic stroke lesion cases and normal cases correctly. Hence AUC 15

0.944

RI PT

ACCEPTED MANUSCRIPT

0.904 0.903

0.943 0.902 0.942

0.901

SC

0.900

Accuracy

DC

0.941

0.940

0.899 0.898 0.897

0.939

M AN U

0.896

0.938

0.895 0.894

0.937

0.893

0.936

0.892

7X7

9X9

11 x 11

13 x 13

Patch size

0.9684

TE

0.9682 0.9680 0.9678 0.9676

EP

0.9674 0.9672 0.9670 0.9668

AC C

0.9666

11 x 11

13 x 13

15 x 15

17 x 17

15 x 15

17 x 17

Patch size

(b)

0.923 0.922 0.921 0.920 0.919 0.918 0.917 0.916 0.915 0.914

0.9662 0.9660

9X9

9X9

0.924

0.9664

7X7

7X7

0.925

Recall

0.9686

Precision

17 x 17

D

(a)

15 x 15

0.913

11 x 11

13 x 13

15 x 15

17 x 17

Patch size

7X7

9X9

11 x 11

13 x 13

Patch size

(c)

(d)

Figure 7: Influence of parameter selection. (a) DC vs varying input patch size; (b) Accuracy vs varying input patch size; (c) Precision vs varying input patch size ; (d) Recall vs varying input patch size.

16

RI PT

ACCEPTED MANUSCRIPT

0.075

0.00259

0.068

0.00258

0.060

0.00257

0.052 0.00256

SC

0.045

MSE

MSE

0.00255

0.00254

0.037

0.030 0.00253

M AN U

0.022

0.00252

0.015

0.00251

0.007

0.00250

0.000

0.5

0.8

1.0

1.3

1.5

1.8

2.0

2.3

(a) 0.075

2.5

2.8

3.0

0.00

0.03

0.05

0.08

0.10

0.13

0.15

0.18

0.20

0.23

0.25

0.28

0.30

(b)

D

0.070 0.065 0.060

TE

0.055 0.050

0.040 0.035

EP

MSE

0.045

0.030 0.025 0.020

AC C

0.015 0.010 0.005 0.000

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

(c)

Figure 8: Effect of MSE on hyper parameter tuning. (a) Varying coefficient of sparsity regularization (β) vs MSE; (b) Varying coefficient of L2-weight regularization (λ) vs MSE; (c) Varying sparsity proportion (ρ) vs MSE.

17

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

Figure 9: Effect of epochs on MSE.

EP

TE

D

is considered as the basis for statistical performance on comparison to different models. Segmentation accuracy is measured in terms of dice overlap metric between the proposed ischemic stroke lesion segmentation model and an expert’s manual ground truth provided in [46]. A DC value of ‘0’ indicates no overlap between the segmented output and manually segmented ground truth, whereas a value of ‘1’ depicts perfect similarity or complete overlap between segmented output and manually segmented ground truth.

AC C

Accuracy =

TP + TN TP + FN + FP + TN

Recall =

TP TP + FN

P recision = Specif icity = DC = 2 ×

(7)

TP TP + FP

(8)

TN TN + FP

(9)

P recision × Recall P recision + Recall 18

(6)

(10)

ACCEPTED MANUSCRIPT

RI PT

Table 3 lists the quantitative evaluation metrics of the proposed model. All the metrics are averaged over the training dataset. Table 3: Performance of the proposed model on the ISLES-SISS 2015 challenge training data. The values correspond to the mean ± standard deviation.

SC

Accuracy AUC DC Precision Recall Specificity 0.904 ± 0.09 0.935 ± 0.128 0.943 ± 0.057 0.968 ± 0.074 0.924 ± 0.072 0.883 ± 0.190

M AN U

Performance evaluation of the proposed SSAE framework is shown in Fig. 10, Fig. 11 and Fig. 12 for ISLES 2015 training dataset. A mean DC metric of 0.943, mean accuracy of 0.904, mean precision of 0.968 and mean recall of 0.924 is obtained. 1.0

0.9

0.8

D

0.6

TE

0.5

0.4

0.3

EP

Dice coefficient

0.7

0.2

AC C

0.1

0.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

Patients

Figure 10: Performance evaluation (DC) of the proposed method on ISLES dataset.

The boxplots for accuracy, AUC, DC, recall and specificity are shown in Fig. 13. Bar graph depicting the performance evaluation of the proposed method with evaluation measures such as precision, DC, recall has been plotted with respect to the other existing ischemic stroke lesion segmentation techniques as shown in Fig. 14. We compared our method with other state-of-the-art sub-acute ischemic stroke lesion segmentation methods 19

ACCEPTED MANUSCRIPT

RI PT

1.0

0.9

0.8

0.7

SC

Accuracy

0.6

0.5

0.3

0.2

0.1

0.0 1

2

3

4

5

6

7

8

9

M AN U

0.4

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

Patients

D

Figure 11: Performance evaluation (Accuracy) of the proposed method on ISLES dataset. 1.0

TE

0.9

0.8

EP

0.7

0.5

AC C

Recall

0.6

0.4

0.3

0.2

0.1

0.0 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

Patients

Figure 12: Performance evaluation (Recall) of the proposed method on ISLES dataset.

20

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

D

Figure 13: Boxplot for performance measures used in the experiment. The dotted line denotes the mean and the red cross denotes the outliers. The black color line inside the rectangle denotes the median of the distribution. 1.00

Precision

0.90

DC

0.85

Recall

TE

0.95

0.80

0.70

0.60 0.55 0.50 0.45 0.40 0.35

EP

0.65

AC C

Perfomance Measures

0.75

0.30 0.25 0.20 0.15 0.10 0.05

d

l.

o h e t M o p

r o P

t

in

F

o

s

r a

n

K

c is

a m n

s e d

it

D u

t

s a

il

s

e t

e t

a

a

l.

l. e t a R e z

e t ie r M r s k a

id v D a

O

R o

b

a

b

o m o

a

l. a

l.

d

e n

e t

e t

a

a

l.

l.

K

o

n

s t

a

Q

n

a

is e r

H

M

a

a

h

e c k

g n L ia

a

T o

C h

m

e n

e t

e t

a

l.

0.00

Figure 14: Comparison of performance measures of proposed method wrt existing methodologies.

21

ACCEPTED MANUSCRIPT

SC

RI PT

using the publicly available ISLES 2015 training dataset (see Table 4). The training data consisted of T1, T2, DWI and FLAIR MR scans of 28 patients with ischemic stroke lesions. It can be inferred from the Table 4 that the performance of the proposed method for classifying a patch into ischemic stroke lesion class or normal class is better than the other techniques. Mean dice coefficient of 0.943 with a standard deviation of 0.057, a mean precision value of 0.968 with a standard deviation of 0.074 and a mean recall value of 0.924 and standard deviation of 0.072 is obtained for the ISLES 2015 dataset.

Method Liang Chen et al. [28] Haeck Tom et al. [55] Qaiser Mahmood et al. [26] David Robben et al. [31] Oskar Maier et al. [27] Reza et al. [56]

Precision 0.52 ± 0.32

Dice Coefficient (DC) 0.55 ± 0.29

Recall 0.69 ± 0.27

0.62 ± 0.31

0.53 ± 0.26

0.56 ± 0.29

0.67 ± 0.33

0.54 ± 0.26

0.50 ± 0.25

0.58 ± 0.33

0.57 ± 0.28

0.68 ± 0.21

0.68 ± 0.32

0.58 ± 0.29

0.58 ± 0.30

0.51 ± 0.25

0.59 ± 0.23

0.79 ± 0.15

0.72 ± 0.31

0.69 ± 0.30

0.67 ± 0.31

D

Francis Dutil et al. [40]

M AN U

Table 4: Comparison of the proposed method with the existing state-of-the-art techniques.

0.77 ± 0.24

0.66 ± 0.24

0.63 ± 0.25

Proposed method

0.968 ± 0.074

0.943 ± 0.057

0.924 ± 0.072

TE

Konstantinos Kamnitsas et al. [41, 43]

AC C

EP

3.3. Qualitative analysis Visual results as shown in Fig. 15 indicates the success of sub-acute ischemic stroke lesion segmentation using the proposed method. Fig. 15 (a) illustrates the input MR images of sub-acute ischemic stroke lesion cases from the ISLES 2015 dataset. The manually segmented ground truth for the corresponding input MR images is depicted in Fig. 15 (b). Reconstructed probability map from the SVM scores and segmented ischemic stroke lesion has been depicted in Fig. 15 (c) and Fig. 15 (d) respectively. The fusion of segmented output from the proposed approach along with the ground truth is shown in Fig. 15 (e).

22

AC C

EP

TE

D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

(a) True Positive

(b)

(c) False Positive

23

(d) False Negative

(e)

AC C

EP

TE

D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

(a) True Positive

(b)

(c) False Positive

(d)

(e)

False Negative

Figure 15: Segmentation results using proposed method. Each row represents a distinct subject. (a) Input MR image; (b) Corresponding ground truth; (c) Probablity map reconstructed from SVM scores; (d) Segmented output; (e) Fusion of segmented output with the ground truth.

24

ACCEPTED MANUSCRIPT

D

M AN U

SC

RI PT

Fig. 16 shows the ischemic stroke lesion detection results from four example subjects that illustrate the steps involved in the proposed approach. The detected ischemic stroke lesions highlighted by the pink color is obtained by the fusion of the input MRI (Fig. 16 (a)) with the corresponding segmented output (Fig. 16 (d)). Our approach depicts lesser false positives and better segmentation accuracy compared to the existing methods. Each row represents different test cases with their respective ground truth and segmented output. Detected lesion

AC C

EP

TE

Detected lesion

(a)

Detected lesion

Detected lesion

(b)

(c)

(d)

(e)

Figure 16: Ischemic stroke lesion detection using the proposed method (a) Input MR image; (b) Corresponding ground truth; (c) Probablity map reconstructed from SVM scores; (d) Segmented output; (e) Fusion of the segmented output with the input MRI. Pink region represents the detected lesions in the corresponding MR images. Best seen in color.

25

ACCEPTED MANUSCRIPT

RI PT

4. Discussion

AC C

EP

TE

D

M AN U

SC

In this paper we presented an unsupervised feature learning approach based on SSAE framework for ischemic stroke lesion segmentation from brain MR images. Existing methods in the literature are based on textural feature extraction [12, 13, 16, 17, 20] and CNN based approaches [40, 41, 42, 43, 45]. The textural feature extraction technique necessitates the need of complex domain knowledge and requires a careful parameter tuning to achieve an optimal segmentation performance. Whereas, CNN based approach requires convolutional and sub-sampling operations to necessitate the feature extraction process. Pre-training of dataset is needed in CNN to tune large number of parameters. Our proposed approach is capable of extracting higher order features in an unsupervised manner using SSAE framework followed by training the SVM classifier in a supervised fashion. The proposed methodology has been tested on the publicly available ISLES 2015 training dataset. It is clear from Table 4 that our proposed approach excels in all performance measures compared to the existing state-of-the-art segmentation methods. Average DC of 0.943 ± 0.057, average precision of 0.968 ± 0.074 and average recall of 0.924 ± 0.072 is achieved using proposed methodology. Majority of the segmentation methods [28, 55, 26, 31, 27, 56] as shown in Table 4, use mainly the textural features which requires a complex domain knowledge for the efficient segmentation of ischemic stroke lesions from the brain MR images. Reza et al. [56] reported a precision of 0.51 ± 0.25, DC of 0.59 ± 0.23 and recall value of 0.79 ± 0.15 and ranked top in contrary to the to the other methods using textural feature extraction. This approach used local textural features and structure based local gradient followed by classification using RF classifier. Francis Dutil et al. [40], Konstantinos Kamnitsas et al. [43] and Zhang et al. [57] used CNN based approach to solve the ischemic stroke lesion segmentation. Francis Dutil et al. [40] reported a precision of 0.72 ± 0.31, DC of 0.69 ± 0.30, and recall of 0.67 ± 0.31 which ranked the best in performance evaluation on ISLES 2015 training dataset. Our approach is 34.44 %, 36.66% and 37.91% times better than Francis Dutil et al. approach in terms of precision, DC and recall measures. To overcome the drawback of computational burden for processing 3D medical scans, a dual pathway based deep 3D CNN model was proposed [43]. This method achieved precision of 0.77, DC of 0.66 and recall of 0.63 which is 20.45%, 30% and 31.81% times lesser than our proposed method. 26

ACCEPTED MANUSCRIPT

M AN U

SC

RI PT

Our proposed approach performs well on classifying the test patch in lesion class label or normal class label and then segmenting the detected ischemic stroke lesion. From Fig. 15, it can be inferred that the false negatives represent the ischemic stroke lesion regions which are not detected by the SSAE framework, whereas false positives are the extra regions that have been identified wrongly as ischemic stroke lesions. Fusion of segmented output and corresponding input MRI has been shown in Fig. 16 (e), which shows the efficieny of our proposed method in segmenting the ischemic stroke lesions. Our approach takes approximately 6 hours for training the classifier model and 10 minutes for testing an MR volume. However, training the classifier model can be parallelized and implemented using GPUs, thereby significantly decreasing the training time. 5. Conclusions

AC C

EP

TE

D

In this work, we implemented an unsupervised feature learning methodology for sub-acute ischemic stroke lesion segmentation using MR images. We designed a five-layer SSAE architecture by extracting features in an unsupervised manner followed by training the SVM classifier in a supervised fashion. Each image patch to be classified was fed into the SSAE model which extracts features and classify the image patch into ischemic stroke lesion or normal patches. A patch based approach was used to extract relevant information from the MR slices and solve the class imbalance problem by randomly sampling an equal number of sample patches from normal and stroke lesion classes. Experimental analysis on the publicly available ISLES 2015 training dataset demonstrates the superior performance of our approach with respect to other state-of-the-art ischemic stroke lesion segmentation methods. The effectiveness of the proposed approach is evident by the significant improvement in the value of precision, DC and recall compared to all previously published methods. Our method achieved an average precision of 0.968, an average DC of 0.943 and an average recall of 0.924 which is 25.71%, 36.67% and 16.96% respectively higher compared to all existing approaches validated on ISLES 2015 dataset. Acknowledgement Authors would like to thank Dr. Ankush Jajodia (Junior Resident, Radiodiagnosis, Goa Medical College, Goa), Dr. Subhash Jakhar (Senior Resi27

ACCEPTED MANUSCRIPT

Compliance with Ethical Standards

RI PT

dent, Neurosurgery, Goa Medical College, Goa), Chetan Srinidhi and Sergio Pereira for rendering their invaluable help in understanding the physiology of brain, concepts related to analysis of MR Images and deep learning methods.

SC

Conflict of interests: All authors declare that he/she has no conflict of interest.

M AN U

Ethical approval: Datasets were obtained from ISLES challenge, there is no live interaction with subjects and the dataset is anonymous. Hence this article does not contain any studies with live human participants or animals performed by any of the authors. References

D

[1] S. Seshadri, P. A. Wolf, Lifetime risk of stroke and dementia: current concepts, and estimates from the framingham study, The Lancet Neurology 6 (12) (2007) 1106–1114.

TE

[2] W. A. Hauser, J. F. Annegers, L. T. Kurland, Incidence of epilepsy and unprovoked seizures in rochester, minnesota: 1935–1984, Epilepsia 34 (3) (1993) 453–458.

EP

[3] C. Sudlow, C. Warlow, Comparable studies of the incidence of stroke and its pathological types, Stroke 28 (3) (1997) 491–499.

AC C

[4] V. L. Feigin, C. M. Lawes, D. A. Bennett, C. S. Anderson, Stroke epidemiology: a review of population-based studies of incidence, prevalence, and case-fatality in the late 20th century, The Lancet Neurology 2 (1) (2003) 43–53.

[5] V. L. Feigin, S. Barker Collo, R. Krishnamurthi, A. Theadom, N. Starkey, Epidemiology of ischaemic stroke and traumatic brain injury, Best practice & research Clinical anaesthesiology 24 (4) (2010) 485–494. [6] V. L. Feigin, C. M. Lawes, D. A. Bennett, S. L. Barker Collo, V. Parag, Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review, The Lancet Neurology 8 (4) (2009) 355–369. 28

ACCEPTED MANUSCRIPT

RI PT

[7] J. A. Chalela, C. S. Kidwell, L. M. Nentwich, M. Luby, J. A. Butman, A. M. Demchuk, M. D. Hill, N. Patronas, L. Latour, S. Warach, Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison, The Lancet 369 (9558) (2007) 293–298.

M AN U

SC

[8] P. Barber, M. Hill, M. Eliasziw, A. Demchuk, J. Pexman, M. Hudon, A. Tomanek, R. Frayne, A. Buchan, Imaging of the brain in acute ischaemic stroke: comparison of computed tomography and magnetic resonance diffusion-weighted imaging, Journal of Neurology, Neurosurgery & Psychiatry 76 (11) (2005) 1528–1533. [9] E. Gleichgerrcht, J. Fridriksson, C. Rorden, L. Bonilha, Connectomebased lesion-symptom mapping (CLSM): a novel approach to map neurological function, NeuroImage: Clinical 16 (2017) 461–467.

TE

D

[10] I. Rekik, S. Allassonni`ere, T. K. Carpenter, J. M. Wardlaw, Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal, NeuroImage: Clinical 1 (1) (2012) 164–178.

EP

[11] O. Maier, C. Schr¨oder, N. D. Forkert, T. Martinetz, H. Handels, Classifiers for ischemic stroke lesion segmentation: a comparison study, PloS one 10 (12) (2015) 1–18.

AC C

[12] A. H. Ali, S. I. Abdulsalam, I. S. Nema, Detection and segmentation of ischemic stroke using textural analysis on brain CT images, International Journal of Scientific & Engineering Research 6 (2) (2015) 396–400. [13] G. Ostrek, A. Przelaskowski, Automatic early stroke recognition algorithm in CT images, Information Technologies in Biomedicine (2012) 101–109. [14] S. Neethu, D. Venkataraman, Stroke detection in brain using CT images, in: Artificial Intelligence and Evolutionary Algorithms in Engineering Systems, Springer, 2015, pp. 379–386. [15] R. Jeena, S. Kumar, A comparative analysis of MRI and CT brain images for stroke diagnosis, in: Emerging Research Areas and 2013 International Conference on Microelectronics, Communications and Renewable 29

ACCEPTED MANUSCRIPT

RI PT

Energy (AICERA/ICMiCR), 2013 Annual International Conference on, IEEE, 2013, pp. 1–5. [16] F. h. Tang, D. K. Ng, D. H. Chow, An image feature approach for computer-aided detection of ischemic stroke, Computers in biology and medicine 41 (7) (2011) 529–536.

M AN U

SC

[17] R. Kanchana, R. Menaka, A novel approach for characterisation of ischaemic stroke lesion using histogram bin-based segmentation and gray level co-occurrence matrix features, The Imaging Science Journal 65 (2) (2017) 124–136. [18] S. Gupta, A. Mishra, R. Menaka, Ischemic stroke detection using image processing and ANN, in: Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on, IEEE, 2014, pp. 1416–1420.

D

[19] A. Shalikar, M. R. Ashouri, M. H. N. Shahraki, A CAD system for automatic classification of brain strokes in CT images, Int J Mechatron Electr Comput Technol 4 (10) (2014) 67–85.

EP

TE

[20] P. Sivakumar, P. Ganeshkumar, An efficient automated methodology for detecting and segmenting the ischemic stroke in brain MRI images, International Journal of Imaging Systems and Technology 27 (3) (2017) 265–272.

AC C

[21] S. Karthikeyan, M. Ezhilarasi, Automatic stroke lesion segmentation from diffusion weighted MRI images, Int J Adv Engg Tech 7 (2) (2016) 111–115. [22] E. d. S. Rebou¸cas, A. M. Braga, R. M. Sarmento, R. C. Marques, P. P. Rebou¸cas Filho, Level set based on brain radiological densities for stroke segmentation in CT images, in: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), IEEE, 2017, pp. 391–396. [23] P. R. Mirajkar, K. A. Bhagwat, A. Singh, M. Ashalatha, Acute ischemic stroke detection using wavelet based fusion of CT and MRI images, in: Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on, IEEE, 2015, pp. 1123–1130. 30

ACCEPTED MANUSCRIPT

RI PT

[24] N. H. Rajini, R. Bhavani, Computer aided detection of ischemic stroke using segmentation and texture features, Measurement 46 (6) (2013) 1865–1874.

SC

[25] R. Karthik, R. Menaka, A multi-scale approach for detection of ischemic stroke from brain MR images using discrete curvelet transformation, Measurement 100 (2017) 223–232.

M AN U

[26] Q. Mahmood, A. Basit, Automatic ischemic stroke lesion segmentation in multi-spectral MRI images using random forests classifier, in: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, 2015, pp. 266–274. [27] O. Maier, M. Wilms, H. Handels, Random forests with selected features for stroke lesion segmentation, Ischemic Stroke Lesion Segmentation (2015) 17–22.

D

[28] L. Chen, P. Bentley, D. Rueckert, A novel framework for sub-acute stroke lesion segmentation based on random forest, Ischemic Stroke Lesion Segmentation (2015) 9–12.

EP

TE

[29] C. W. Wang, J. H. Lee, Stroke lesion segmentation of 3D brain mri using multiple random forests and 3D registration, in: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, 2015, pp. 222–232.

AC C

[30] J. C. Griffis, J. B. Allendorfer, J. P. Szaflarski, Voxel-based gaussian na¨ıve bayes classification of ischemic stroke lesions in individual T1weighted MRI scans, Journal of neuroscience methods 257 (2016) 97– 108. [31] D. Robben, D. Christiaens, J. R. Rangarajan, J. Gelderblom, P. Joris, F. Maes, P. Suetens, A voxel-wise, cascaded classification approach to ischemic stroke lesion segmentation, in: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, 2015, pp. 254–265. [32] H. L. Halme, A. Korvenoja, E. Salli, segmentation of stroke lesions using spatial normalization, random forest classification and contextual

31

ACCEPTED MANUSCRIPT

RI PT

clustering, in: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, 2015, pp. 211–221.

SC

[33] O. Maier, M. Wilms, J. von der Gablentz, U. M. Kr¨amer, T. F. M¨ unte, H. Handels, Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences, Journal of neuroscience methods 240 (2015) 89–100.

M AN U

[34] M. Goetz, C. Weber, C. Kolb, K. Maier Hein, Input data adaptive learning (IDAL) for sub-acute ischemic stroke lesion segmentation, in: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, 2015, pp. 284–295. [35] B. de Haan, P. Clas, H. Juenger, M. Wilke, H. O. Karnath, Fast semiautomated lesion demarcation in stroke, NeuroImage: Clinical 9 (2015) 69–74.

TE

D

[36] V. Alex, M. S. KP, S. S. Chennamsetty, G. Krishnamurthi, Generative adversarial networks for brain lesion detection, in: SPIE Medical Imaging, International Society for Optics and Photonics, 2017, pp. 101330G– 101330G.

EP

[37] V. Alex, K. Vaidhya, S. Thirunavukkarasu, C. Kesavadas, G. Krishnamurthi, Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation, Journal of Medical Imaging 4 (4) (2017) 1–12.

AC C

[38] Y. S. Tyan, M. C. Wu, C. L. Chin, Y. L. Kuo, M. S. Lee, H. Y. Chang, Ischemic stroke detection system with a computer-aided diagnostic ability using an unsupervised feature perception enhancement method, Journal of Biomedical Imaging 2014 (2014) 19. [39] L. Chen, P. Bentley, D. Rueckert, Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks, NeuroImage: Clinical 15 (2017) 633–643. [40] F. Dutil, M. Havaei, C. Pal, H. Larochelle, P. Jodoin, A convolutional neural network approach to brain leison segmentation, Ischemic Stroke Lesion Segmentation (2015) 51–56. 32

ACCEPTED MANUSCRIPT

RI PT

[41] K. Kamnitsas, L. Chen, C. Ledig, D. Rueckert, B. Glocker, Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI, Ischemic Stroke Lesion Segmentation (2015) 13–16.

SC

[42] Y. Choi, Y. Kwon, H. Lee, B. J. Kim, M. C. Paik, J. H. Won, Ensemble of Deep Convolutional Neural Networks for prognosis of ischemic stroke, in: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, 2016, pp. 231–243.

M AN U

[43] K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, B. Glocker, Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Medical image analysis 36 (2017) 61–78. [44] S. Wang, Y. Jiang, X. Hou, H. Cheng, S. Du, Cerebral microbleed detection based on the convolution neural network with rank based average pooling, IEEE Access 5 (2017) 16576–16583. doi:10.1109/ACCESS.2017.2736558.

TE

D

[45] H. Shen, S. Manivannan, R. Annunziata, R. Wang, J. Zhang, Combination of CNN and hand-crafted feature for ischemic stroke lesion segmentation, Ischemic Stroke Lesion Segmentation (2016) 1.

EP

[46] O. Maier, B. H. Menze, J. von der Gablentz, L. H¨ani, M. P. Heinrich, M. Liebrand, S. Winzeck, A. Basit, P. Bentley, L. Chen, et al., Isles 2015A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI, Medical image analysis 35 (2017) 250–269.

AC C

[47] N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, J. C. Gee, N4ITK: improved N3 bias correction, IEEE transactions on medical imaging 29 (6) (2010) 1310–1320. [48] A. Hertzmann, C. E. Jacobs, N. Oliver, B. Curless, D. H. Salesin, Image analogies, in: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, ACM, 2001, pp. 327–340. [49] A. J. Bell, T. J. Sejnowski, The independent components of natural scenes are edge filters, Vision research 37 (23) (1997) 3327–3338.

33

ACCEPTED MANUSCRIPT

RI PT

[50] P. Baldi, Autoencoders, unsupervised learning, and deep architectures, in: Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 2012, pp. 37–49. [51] A. Ng, Sparse autoencoder, CS294A Lecture notes 72 (2011) 1–19. K-sparse autoencoders,

arXiv preprint

SC

[52] A. Makhzani, B. Frey, arXiv:1312.5663.

M AN U

[53] S. Kullback, R. A. Leibler, On information and sufficiency, The annals of mathematical statistics 22 (1) (1951) 79–86. [54] Y. Bengio, Learning deep architectures for AI, Foundations and trends in Machine Learning 2 (1) (2009) 1–127. [55] T. Haeck, F. Maes, P. Suetens, Automated model-based segmentation of ischemic stroke in MR images, in: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, 2015, pp. 246–253.

TE

D

[56] S. M. Reza, L. Pei, K. Iftekharuddin, Ischemic stroke lesion segmentation using local gradient and texture features, Ischemic Stroke Lesion Segmentation (2015) 23–26.

AC C

EP

[57] R. Zhang, L. Zhao, W. Lou, J. M. Abrigo, V. C. Mok, W. C. Chu, D. Wang, L. Shi, Automatic segmentation of acute ischemic stroke from DWI using 3D Fully Convolutional DenseNets, IEEE Transactions on Medical Imaging (2018) 1–1doi:10.1109/TMI.2018.2821244.

34

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

Conflict of Interest: All authors declare that he/she has no conflict of interest.