The conventional FCM algorithm and some existing variants are either sensitive to noise or prone to loss of details . This paper presents a modified FCM algorithm that incorporates bilateral filtering for medical image segmentation . The experimental results and quantitative analyses suggest that , compared to the conventional FCM , the proposed method improves clustering performance with higher standard of noise-resistance and detail-preservation . The main difference with previous EM-based segmentation algorithms is that we employ an anisotropic Markov random field , which incorporates preferred directions based on the underlying image structure and coherence . Results on synthetic and ONH images show the ability of the algorithm to accurately segment these structures in presence of significant artifacts , and suggest its suitability to other medical imaging segmentation tasks . Atherosclerosis is one of the causes of coronary heart disease ( CHD ) . The proposed marker controlled watershed algorithm for medical image segmentation and analysis is very important because of its advantages , such as always being able to construct an entire division of the color image and prevent over segmentation as compared to conventional watershed algorithm . Paper finds Region of Interest ( ROI ) values of segmented image with proposed technique for coronary atherosclerosis . Organ segmentation is often a first step in medical diagnostic . In this paper a fully automatic three-dimensional method for liver segmentation is presented . It is based on voxel density analysis with use of automated grow cut method . This paper presents an improved Snake model algorithm , which initializes contour by maker control watershed algorithm and adds an external force related with the curve shape into the energy function of Snake model to solve the problem about curvature constraints . Testing on the Magnetic Resonance ( MR ) images of the abdomen for liver image segmentation proves that the new algorithm can achieve good results . In general , segmentation techniques for Liver images can be semi-automatic and fully automatized in nature . In this paper , various approaches , applications and its related problems has been explained for the fully automatized techniques of liver image segmentation . Objective To achieve the liver region segmentation from liver CT image to assist in computer-aided therapy , pathological changed tissue localization , three dimensional reconstruction , therapeutic regimen design and so on . Methods Neighborhood connected algorithm based on region growing was used to realize the liver CT(computer tomography ) image segmentation . In this paper , we present a GVF-Snake based method for liver region segmentation on CT images . Firstly , the CT images are enhanced using a method of histogram equalization and anisotropic diffusion filter . However , when this concept is ported to the medical imaging domain , reading expertise will have a direct impact on the annotation accuracy . In this study , we examine the impact of expertise and the amount of available annotations on the accuracy outcome of a liver segmentation problem in an abdominal computed tomography ( CT ) image database . In controlled experiments , we study this impact for different types of weak annotations . In the past decades , many liver segmentation approaches had been proposed . Yet , the liver segmentation problem remains open . More robust approaches need to developed and new possibilities should be investigated . In order to solve the problem of traditional manual method for seed point selection in liver segmentation process , an automatic seed point selection method was proposed based on statistic model of intensity . This method is according to the characteristics of structure and grayscale of the liver . Next , a propagation learning approach is incorporated into a semi-automatic classification method for robust segmentation segmentation of liver tumors based on liver ROI obtained . Finally , an unbiased probabilistic liver atlas construction technique is adopted to embody the shape and intensity variation to constrain liver segmentation segmentation . We also report preliminary experimental results . Secondly , we locate the cube-based liver shapes in all sub-volumes via Hough voting and dynamic programming . Thirdly , we construct the basic 3D model through a level-set framework for liver shape segmentation . Finally , to introduce neighbors statistical analysis into the above model , we deform the 3D liver shape to overcome disturbances caused by noise and inhomogeneity . This paper presents discussions on experimental result eval- uation outcomes of a new liver volume segmentation method developed for 10 specified CT image datasets . Precise liver surface segmentation is the first step and one of the major tasks in individual surgical resection virtual reality simulations . There are five major difficulties : Firstly , the automatic initialization of liver detection is often unreliable . The importance of retinal vessel segmentation is particularly emphasized in such applications . An extensive review of previous work on retinal vessel segmentation and salient texture analysis methods is presented . Five automatic retinal vessel segmentation methods are proposed in this thesis . We review the related publications since 1982 , which include more than 80 papers for retinal vessels detections in the research scope spanning from segmentation to classification . Although deep learning has been successfully implemented in other areas , we found only 17 papers so far focus on retinal blood vessel segmentation . This paper characterizes each deep learning based segmentation method as described in the literature . This paper brings together these two techniques and propose to train arbitrarily designed networks , with a focus on relatively small databases , in two stages : patch pre-training and full sized image fine-tuning . An experimental work have been carried out on the tasks of retinal blood vessel segmentation and the optic disc one , using four publicly available databases . Furthermore , three types of network are considered , going from a very light weighted network to a densely connected one . In this paper , a supervised method is presented based on a pre-trained fully convolutional network through transfer learning . This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition and result merging . Meanwhile , additional unsupervised image post-processing techniques are applied to this proposed method so as to refine the final result . Before detecting the features and abnormalities in the retinal image , preprocessing is required . Retinal image vessel segmentation and their branching pattern are used for automated screening and diagnosis of diabetic retinopathy . We propose a method for color retinal image preprocessing i.e. creating a binary mask to remove the noisy area and background from retinal image . This paper proposes a neural network based supervised segmentation algorithm for retinal vessel delineation . The histogram of each training image patch and its optimal threshold acquired through iteratively comparing the binaryzation result to the manual segmentation are applied to a BP neural network to establish the correspondence between the intensity distribution and optimal segmentation parameter . Retinal vessel delineation is a hot research topic owing to its importance in a lot of clinic application . Several methods have been proposed in the past decades . In the framework of computer-aided diagnosis of eye disease , a new contextual image feature named influence degree of average intensity is proposed for retinal vessel image segmentation . This new feature evaluates the influence degree of current detected pixel decreasing the average intensity of the local row where that pixel located . Retinal vessel extraction and segmentation is essential for supporting diagnosis of eye-related diseases . In recent years , deep learning has been applied to vessel segmentation and achieved excellent performance . Initial image features are projected onto the echo state network reservoir which maps them , through its internal nodes activations , into a new set of features to be classified into vessel or non-vessel by the echo state network readout which consists , in the proposed approach , of a multi-layer perceptron . Experimental results on the publicly available DRIVE dataset , commonly used in retinal vessel segmentation research , demonstrate the ability of the proposed method in achieving promising performance results in terms of both segmentation accuracy and efficiency . Training accuracy of the proposed models of RU - Net , and R2U - Net against ResU - Net and U - Net . section : C. Results 1 ) Retina Blood Vessel Segmentation Using the DRIVE Dataset The precise segmentation results achieved with the proposed R2U - Net model are shown in Fig . 8 . The famous encoder-decoder convolutional neural network , which although has made great achievements in medical image segmentation areas , may fail to address these challenges due to the insufficient uses of multi-scale features and context information . To address these challenges , this paper proposes a Cross-Level fusion and Context Inference Network ( CLCI-Net ) for the chronic stroke lesion segmentation from T1-weighted MR images . Specifically , a Cross-Level feature Fusion ( CLF ) strategy was developed to make full use of different scale features across different levels ; Extending Atrous Spatial Pyramid Pooling ( ASPP ) with CLF , we have enriched multi-scale features to handle the different lesion sizes ; In addition , convolutional long short-term memory ( ConvLSTM ) is employed to infer context information and thus capture fine structures to address the intensity similarity issue . Acute stroke lesion segmentation and prediction tasks are of great clinical interest as they can help doctors make better informed time-critical treatment decisions . Automatic segmentation of these lesions is a complex task due to their heterogeneous appearance , dynamic evolution and inter-patient differences . Typically , acute stroke lesion tasks are approached with methods developed for chronic stroke or other brain lesions . However , the pathophysiology and anatomy of acute stroke establishes an inherently different problem that needs special consideration . BACKGROUND AND OBJECTIVES Acute stroke lesion segmentation is of paramount importance as it can aid medical personnel to render a quicker diagnosis and administer consequent treatment . Automation of this task is technically exacting due to the variegated appearance of lesions and their dynamic development , medical discrepancies , unavailability of datasets , and the requirement of several MRI modalities for imaging . Third phase includes image registration and fusion of CT and MRI image . Segmentation of stroke lesion from fused image is performed in fourth phase . The performance evaluation of fusion is computed using Peak Signal to Noise Ratio ( PSNR ) and Root Mean Square Error ( RMSE ) . However , the subtle expression of ischemia in acute CT images has made it hard for automated methods to extract potentially quantifiable information . In this work , we present and evaluate an automated deep learning tool for acute stroke lesion core segmentation from CT and CT perfusion images . For evaluation , the Ischemic Stroke Lesion Segmentation ( ISLES ) 2018 challenge dataset is used that includes 94 cases for training and 62 for testing . Using DWI examination , this paper proposes an application of image processing in a computer-aided diagnosis system , which can effectively calculate the volume size and provide 3D reconstruction data of a lesion . The potential benefits of using our system include the higher accuracy of acute stroke lesion definition , the reduced time and procedure of calculating the volume , and providing 3D reconstruction image of stroke patients , which can effectively assist doctors in making more accurate diagnoses and treating patients in a more convenient way . Compared with the traditional method , the experimental results have shown the superior performance of this proposed system . Purpose The aim of this study was to investigate if 3D convolutional deep neural network implementation DeepMedic [ 1 ] could be applied to computed tomography angiography ( CTA ) images in acute stroke lesion detection . DeepMedic has previously been successful in lesion segmentation from magnetic resonance images [ 2 ] . Next , the features from wavelet transform are extracted from the region and fed into the rule-based classifier . Results demonstrated that FCM with correlation template offered the best performance for acute stroke segmentation with the highest rate of 0.77 Jaccard index . The classification accuracy for acute stroke , tumor , chronic stroke and necrosis are 94 % , 97 , 63 % and 60 % . Ischemic stroke occurs through a blockage of clogged blood vessels supplying blood to the brain . Segmentation of the stroke lesion is vital to improve diagnosis , outcome assessment and treatment planning . In this work , we propose a segmentation model with adversarial learning for ischemic lesion segmentation . Automated methods for segmentation of ischemic stroke lesions could significantly reduce the workload of radiologists and speed up the beginning of patient treatment . In this paper , we present a method for subacute ischemic stroke lesion segmentation from multispectral magnetic resonance images ( MRI ) . The method involves classification of voxels with a Random Forest algorithm and subsequent classification refinement with contextual clustering .
The conventional FCM algorithm and some existing variants are either sensitive to noise or prone to loss of details . This paper presents a modified FCM algorithm that incorporates bilateral filtering for medical image segmentation . The experimental results and quantitative analyses suggest that , compared to the conventional FCM , the proposed method improves clustering performance with higher standard of noise-resistance and detail-preservation .
The main difference with previous EM-based segmentation algorithms is that we employ an anisotropic Markov random field , which incorporates preferred directions based on the underlying image structure and coherence . Results on synthetic and ONH images show the ability of the algorithm to accurately segment these structures in presence of significant artifacts , and suggest its suitability to other medical imaging segmentation tasks .
Atherosclerosis is one of the causes of coronary heart disease ( CHD ) . The proposed marker controlled watershed algorithm for medical image segmentation and analysis is very important because of its advantages , such as always being able to construct an entire division of the color image and prevent over segmentation as compared to conventional watershed algorithm . Paper finds Region of Interest ( ROI ) values of segmented image with proposed technique for coronary atherosclerosis .
Organ segmentation is often a first step in medical diagnostic . In this paper a fully automatic three-dimensional method for liver segmentation is presented . It is based on voxel density analysis with use of automated grow cut method .
This paper presents an improved Snake model algorithm , which initializes contour by maker control watershed algorithm and adds an external force related with the curve shape into the energy function of Snake model to solve the problem about curvature constraints . Testing on the Magnetic Resonance ( MR ) images of the abdomen for liver image segmentation proves that the new algorithm can achieve good results .
In general , segmentation techniques for Liver images can be semi-automatic and fully automatized in nature . In this paper , various approaches , applications and its related problems has been explained for the fully automatized techniques of liver image segmentation .
Objective To achieve the liver region segmentation from liver CT image to assist in computer-aided therapy , pathological changed tissue localization , three dimensional reconstruction , therapeutic regimen design and so on . Methods Neighborhood connected algorithm based on region growing was used to realize the liver CT(computer tomography ) image segmentation .
In this paper , we present a GVF-Snake based method for liver region segmentation on CT images . Firstly , the CT images are enhanced using a method of histogram equalization and anisotropic diffusion filter .
However , when this concept is ported to the medical imaging domain , reading expertise will have a direct impact on the annotation accuracy . In this study , we examine the impact of expertise and the amount of available annotations on the accuracy outcome of a liver segmentation problem in an abdominal computed tomography ( CT ) image database . In controlled experiments , we study this impact for different types of weak annotations .
In the past decades , many liver segmentation approaches had been proposed . Yet , the liver segmentation problem remains open . More robust approaches need to developed and new possibilities should be investigated .
In order to solve the problem of traditional manual method for seed point selection in liver segmentation process , an automatic seed point selection method was proposed based on statistic model of intensity . This method is according to the characteristics of structure and grayscale of the liver .
Next , a propagation learning approach is incorporated into a semi-automatic classification method for robust segmentation segmentation of liver tumors based on liver ROI obtained . Finally , an unbiased probabilistic liver atlas construction technique is adopted to embody the shape and intensity variation to constrain liver segmentation segmentation . We also report preliminary experimental results .
Secondly , we locate the cube-based liver shapes in all sub-volumes via Hough voting and dynamic programming . Thirdly , we construct the basic 3D model through a level-set framework for liver shape segmentation . Finally , to introduce neighbors statistical analysis into the above model , we deform the 3D liver shape to overcome disturbances caused by noise and inhomogeneity .
This paper presents discussions on experimental result eval- uation outcomes of a new liver volume segmentation method developed for 10 specified CT image datasets . Precise liver surface segmentation is the first step and one of the major tasks in individual surgical resection virtual reality simulations . There are five major difficulties : Firstly , the automatic initialization of liver detection is often unreliable .
The importance of retinal vessel segmentation is particularly emphasized in such applications . An extensive review of previous work on retinal vessel segmentation and salient texture analysis methods is presented . Five automatic retinal vessel segmentation methods are proposed in this thesis .
We review the related publications since 1982 , which include more than 80 papers for retinal vessels detections in the research scope spanning from segmentation to classification . Although deep learning has been successfully implemented in other areas , we found only 17 papers so far focus on retinal blood vessel segmentation . This paper characterizes each deep learning based segmentation method as described in the literature .
This paper brings together these two techniques and propose to train arbitrarily designed networks , with a focus on relatively small databases , in two stages : patch pre-training and full sized image fine-tuning . An experimental work have been carried out on the tasks of retinal blood vessel segmentation and the optic disc one , using four publicly available databases . Furthermore , three types of network are considered , going from a very light weighted network to a densely connected one .
In this paper , a supervised method is presented based on a pre-trained fully convolutional network through transfer learning . This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition and result merging . Meanwhile , additional unsupervised image post-processing techniques are applied to this proposed method so as to refine the final result .
Before detecting the features and abnormalities in the retinal image , preprocessing is required . Retinal image vessel segmentation and their branching pattern are used for automated screening and diagnosis of diabetic retinopathy . We propose a method for color retinal image preprocessing i.e. creating a binary mask to remove the noisy area and background from retinal image .
This paper proposes a neural network based supervised segmentation algorithm for retinal vessel delineation . The histogram of each training image patch and its optimal threshold acquired through iteratively comparing the binaryzation result to the manual segmentation are applied to a BP neural network to establish the correspondence between the intensity distribution and optimal segmentation parameter .
Retinal vessel delineation is a hot research topic owing to its importance in a lot of clinic application . Several methods have been proposed in the past decades .
In the framework of computer-aided diagnosis of eye disease , a new contextual image feature named influence degree of average intensity is proposed for retinal vessel image segmentation . This new feature evaluates the influence degree of current detected pixel decreasing the average intensity of the local row where that pixel located .
Retinal vessel extraction and segmentation is essential for supporting diagnosis of eye-related diseases . In recent years , deep learning has been applied to vessel segmentation and achieved excellent performance .
Initial image features are projected onto the echo state network reservoir which maps them , through its internal nodes activations , into a new set of features to be classified into vessel or non-vessel by the echo state network readout which consists , in the proposed approach , of a multi-layer perceptron . Experimental results on the publicly available DRIVE dataset , commonly used in retinal vessel segmentation research , demonstrate the ability of the proposed method in achieving promising performance results in terms of both segmentation accuracy and efficiency .
Training accuracy of the proposed models of RU - Net , and R2U - Net against ResU - Net and U - Net . section : C. Results 1 ) Retina Blood Vessel Segmentation Using the DRIVE Dataset The precise segmentation results achieved with the proposed R2U - Net model are shown in Fig . 8 .
The famous encoder-decoder convolutional neural network , which although has made great achievements in medical image segmentation areas , may fail to address these challenges due to the insufficient uses of multi-scale features and context information . To address these challenges , this paper proposes a Cross-Level fusion and Context Inference Network ( CLCI-Net ) for the chronic stroke lesion segmentation from T1-weighted MR images . Specifically , a Cross-Level feature Fusion ( CLF ) strategy was developed to make full use of different scale features across different levels ; Extending Atrous Spatial Pyramid Pooling ( ASPP ) with CLF , we have enriched multi-scale features to handle the different lesion sizes ; In addition , convolutional long short-term memory ( ConvLSTM ) is employed to infer context information and thus capture fine structures to address the intensity similarity issue .
Acute stroke lesion segmentation and prediction tasks are of great clinical interest as they can help doctors make better informed time-critical treatment decisions . Automatic segmentation of these lesions is a complex task due to their heterogeneous appearance , dynamic evolution and inter-patient differences . Typically , acute stroke lesion tasks are approached with methods developed for chronic stroke or other brain lesions . However , the pathophysiology and anatomy of acute stroke establishes an inherently different problem that needs special consideration .
BACKGROUND AND OBJECTIVES Acute stroke lesion segmentation is of paramount importance as it can aid medical personnel to render a quicker diagnosis and administer consequent treatment . Automation of this task is technically exacting due to the variegated appearance of lesions and their dynamic development , medical discrepancies , unavailability of datasets , and the requirement of several MRI modalities for imaging .
Third phase includes image registration and fusion of CT and MRI image . Segmentation of stroke lesion from fused image is performed in fourth phase . The performance evaluation of fusion is computed using Peak Signal to Noise Ratio ( PSNR ) and Root Mean Square Error ( RMSE ) .
However , the subtle expression of ischemia in acute CT images has made it hard for automated methods to extract potentially quantifiable information . In this work , we present and evaluate an automated deep learning tool for acute stroke lesion core segmentation from CT and CT perfusion images . For evaluation , the Ischemic Stroke Lesion Segmentation ( ISLES ) 2018 challenge dataset is used that includes 94 cases for training and 62 for testing .
Using DWI examination , this paper proposes an application of image processing in a computer-aided diagnosis system , which can effectively calculate the volume size and provide 3D reconstruction data of a lesion . The potential benefits of using our system include the higher accuracy of acute stroke lesion definition , the reduced time and procedure of calculating the volume , and providing 3D reconstruction image of stroke patients , which can effectively assist doctors in making more accurate diagnoses and treating patients in a more convenient way . Compared with the traditional method , the experimental results have shown the superior performance of this proposed system .
Purpose The aim of this study was to investigate if 3D convolutional deep neural network implementation DeepMedic [ 1 ] could be applied to computed tomography angiography ( CTA ) images in acute stroke lesion detection . DeepMedic has previously been successful in lesion segmentation from magnetic resonance images [ 2 ] .
Next , the features from wavelet transform are extracted from the region and fed into the rule-based classifier . Results demonstrated that FCM with correlation template offered the best performance for acute stroke segmentation with the highest rate of 0.77 Jaccard index . The classification accuracy for acute stroke , tumor , chronic stroke and necrosis are 94 % , 97 , 63 % and 60 % .
Ischemic stroke occurs through a blockage of clogged blood vessels supplying blood to the brain . Segmentation of the stroke lesion is vital to improve diagnosis , outcome assessment and treatment planning . In this work , we propose a segmentation model with adversarial learning for ischemic lesion segmentation .
Automated methods for segmentation of ischemic stroke lesions could significantly reduce the workload of radiologists and speed up the beginning of patient treatment . In this paper , we present a method for subacute ischemic stroke lesion segmentation from multispectral magnetic resonance images ( MRI ) . The method involves classification of voxels with a Random Forest algorithm and subsequent classification refinement with contextual clustering .
Fuzzy C-means Clustering with Bilateral Filtering for Medical Image Segmentation Segmentation of connective tissue in the optic nerve head using an anisotropic Markov random field Application of Segmentation Techniques on Atherosclerosis Images An automatic liver segmentation algorithm based on grow cut and level sets Improved Snake Model Algorithm with Application in Liver Image Segmentation Automated Segmentation of CT Liver Images: A Review Liver CT image segmentation based on region growing GVF-Snake Based Method for Liver Region Segmentation Employing Weak Annotations for Medical Image Analysis Problems A novel approach for automatic and robust segmentation of the 3D liver in computed tomography images Automatic Seed Point Selection for Liver Segmentation Liver Workbench: A Tool Suite for Liver and Liver Tumor Segmentation and Modeling Automatic liver segmentation from CT images using latent semantic indexing A Discussion on the Evaluation of A New Automatic Liver Volume Segmentation Method for Specified CT Image Datasets Retinal vessel segmentation using textons Deep Learning Models for Retinal Blood Vessels Segmentation: A Review From Patch to Image Segmentation using Fully Convolutional Networks - Application to Retinal Images Retinal blood vessel segmentation using fully convolutional network with transfer learning PREPROCESSING AND BLOOD VESSEL SEGMENTATION OF RETINAL IMAGES Supervised segmentation of vasculature in retinal images using neural networks A supervised method using convolutional neural networks for retinal vessel delineation Selective Search and Intensity Context Based Retina Vessel Image Segmentation An Unsupervised Retinal Vessel Extraction and Segmentation Method Based On a Tube Marked Point Process Model Blood vessel segmentation in retinal images using echo state networks Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke SUNet: a deep learning architecture for acute stroke lesion segmentation and outcome prediction in multimodal MRI CSNet: A new DeepNet framework for ischemic stroke lesion segmentation Acute ischemic stroke detection using wavelet based fusion of CT and MRI images Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks The Diagnostic Application of Brain Image Processing and Analysis System for Ischemic Stroke [OA214] An experience with open source machine learning software deepmedic AUTOMATED BRAIN LESION CLASSIFICATION USING HYBRID FUZZY C-MEANS WITH CORRELATION TEMPLATE AND WAVELET TRANSFORM Ischemic Stroke Lesion Segmentation Using Adversarial Learning ISLES (SISS) Challenge 2015: Segmentation of Stroke Lesions Using Spatial Normalization, Random Forest Classification and Contextual Clustering
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HAIS (1) ISBI MMSP ICASSP ICACI ArXiv ICACCI BrainLes@MICCAI (1) Brainles@MICCAI
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