This paper introduces a contiguity-constrained algorithm with an aggregation index which uses neighbouring relations present in the data . Experiments show the behaviour of the proposed method in the case of medical image segmentation . However , it is also possible to use linear combinations of training images and labels to augment the dataset using the recently-proposed ‘ mixup ’ algorithm . Here , we apply this algorithm for use in medical imaging segmentation . We show that it increases performance in segmentation tasks , and also offer a theoretical suggestion for the efficacy of this technique . Medical imaging segmentation provides vital information for surgical diagnosis , and usually demands an accurate segmentation . A fully automated computed tomography image segmentation method is proposed . Manual segmentation of brain tumor from 3D multimodal magnetic resonance images ( MRI ) is time-consuming task and leading to human errors . In this paper , two automated approaches has been developed for brain tumor segmentation to discuss which one will provide accurate segmentation that is close to the manual results . The MR feature images used for the segmentation consist of three weighted images ( enhanced T1 , proton density(PD ) and T2 ) for each axial slice through the head . A fully automatic segmentation algorithm based on local and global consistency with robust label initialization is proposed for brain tumor segmentation in multi-parametric MR images . A tumor probability map is first computed using support vector machine ( SVM ) classification of voxel-wise features , and then aggregated with respect to the image intrinsic structures in a multi-scale manner . Earlier detection , diagnosis and proper treatment of brain tumor are essential to prevent human death . An effective brain tumor detection and segmentation using MR image is an essential task in medical field . A number of research papers related to medical image segmentation methods are studied . Segmentation of brain tumor MR images is the most difficult task as the tumor varies in terms of size , shape , location , and texture . In this paper , we discuss various supervised and unsupervised techniques for brain tumor detection and segmentation such as K-nearest neighbor ( K-NN ) , K-means clustering , and using morphological operators . We also review the results obtained . Image segmentation is considered as one of the most crucial techniques for visualizing tissues in human being . In considering brain tumor image segmentation , manually with an expert , it is more likely that the errors are present in it . To automate image segmentation , we have proposed an algorithm to obtain a global thresholding value for a particular image . Brain tumor image segmentation has been a time-consuming and laborious task for decades . Since deep learning has been applied to the image field , there have been many efforts to find automatic segmentation methods . Computerized screening and diagnosis of brain tumor is an intensively investigated domain , especially since the first Brain Tumor Segmentation Challenge ( BraTS ) organized seven years ago . Several ensemble learning solutions have been proposed lately to the brain tumor segmentation problem . This paper presents an evaluation framework designed to test the accuracy and efficiency of ensemble learning algorithms deployed for brain tumor segmentation using the BraTS 2016 train data set . Various evaluation parameters like Dice index , Jaccard index , Sensitivity , Specificity are calculated for all the algorithms and comparative analysis is carried out . Experimental results state that Fuzzy C-means clustering outperforms K-means and Gaussian Mixture Model algorithm for brain tumor segmentation problem . Fully automatic brain tumor segmentation is one of the critical tasks in magnetic resonance imaging ( MRI ) images . This proposed work is aimed to develop an automatic method for brain tumor segmentation process by wavelet transformation and clustering technique . The proposed method using discrete wavelet transform ( DWT ) for pre‐ and post‐processing , fuzzy c‐means ( FCM ) for brain tissues segmentation . Moreover , all segmented brain tumor images are reconstructed and located to three dimensions . Experimental results show that the algorithm based on Chan-Vese model can solve the problem of incomplete segmentation easily occurred in brain tumor segmentation process . At the same time , the 3D reconstruction and location of the brain tumor offer a great reference value in clinical work . The analysis of brain MRI is an important part of patient care and decision . Hence in the proposed Clinical Support System , the brain MRI image is preprocessed using Genetic Optimized Median Filter followed by brain tumor region segmentation using Hierarchical Fuzzy Clustering Algorithm . The features of the tumor region are extracted through GLCM feature extraction method . Brain tumor segmentation and labeling is a challenging task in medical imaging . In this paper , a novel patch based dictionary learning algorithm for automatic multi-label brain tumor segmentation is proposed . Currently several methods exist for tumor segmentation but they all lack high accuracy . Here we present a solution for brain tumor segmenting by using deep learning . In this work , we studied different angles of brain MR images and applied different networks for segmentation . document : Brain Tumor Segmentation with Deep Neural Networks ti In this paper , we present a fully automatic brain tumor segmentation method based on Deep Neural Networks ( DNNs ) . These tumors ( together with their surrounding edema ) are often diffused , poorly contrasted , and extend tentacle - like structures that make them difficult to segment . Another fundamental difficulty with segmenting brain tumors is that they can appear anywhere in the brain , in almost any shape and size . Furthermore , unlike images derived from X - ray computed tomography ( CT ) scans , the scale of voxel values in MR images is not standardized . section : Related work As noted by , the number of publications devoted to automated brain tumor segmentation has grown exponentially in the last several decades . This observation not only underlines the need for automatic brain tumor segmentation tools , but also shows that research in that area is still a work in progress . Brain tumor segmentation methods ( especially those devoted to MRI ) can be roughly divided in two categories : those based on generative models and those based on discriminative models . By employing dense - inference with 3D CNNs , and reported computation times of a few seconds and approximately a minute respectively for the processing of a single brain scan . Even though the size of their developed networks was limited , a factor that is directly related to a network ’s representational power , their results on MS and brain tumor segmentation respectively were very promising . Performance of CNNs is significantly influenced by the strategy for extracting training samples . [ b ] 1.0 [ b ] 1.0 subsection : Brain Tumor Segmentation subsubsection : Material and Pre - Processing document : 3D MRI brain tumor segmentation using autoencoder regularization Automated segmentation of brain tumors from 3D magnetic resonance images ( MRIs ) is necessary for the diagnosis , monitoring , and treatment planning of the disease . Manual delineation practices require anatomical knowledge , are expensive , time consuming and can be inaccurate due to human error . Convolutional neural networks ( CNN ) are able to learn from examples and demonstrate state - of - the - art segmentation accuracy both in 2D natural images and in 3D medical image modalities . Multimodal Brain Tumor Segmentation Challenge ( BraTS ) aims to evaluate state - of - the - art methods for the segmentation of brain tumors by providing a 3D MRI dataset with ground truth tumor segmentation labels annotated by physicians . This year , BraTS 2018 training dataset included 285 cases ( 210 HGG and 75 LGG ) , each with four 3D MRI modalities ( T1 , T1c , T2 and FLAIR ) rigidly aligned , resampled to 1x1x1 mm isotropic resolution and skull - stripped . Our model is evaluated on the BRATS 2015 benchmark for brain tumor segmentation , where it obtains state-of-the-art results , by surpassing recently published results reported in Med Image Anal 43 : 98–111 , 2018 , Med Image Anal 36:61–78 , 2017 . We further applied the proposed Brain SegNet for ischemic stroke lesion outcome prediction , with impressive results achieved on the Ischemic Stroke Lesion Segmentation ( ISLES ) 2017 database . This procedure can introduce misidentification issue and time consuming . In this paper , a method for automated classification of PF slices in CT brain images is investigated to sort the slice that contains PF for further study of ischemic stroke detection . This framework employs 11 features of Gray Level Co-Occurrence Matrix ( GLCM ) . In this paper , to improve the accuracy of detection and segmentation , we modify U-Net architecture to address ischemic stroke segmentation and detection , from ISLES 2018 dataset . In this dataset , CT images ( in five modalities ) and corresponding ground truth created by combining manual annotations are provided . Within this context , low rank-&-sparse matrix decomposition ( also known as robust PCA , RPCA ) and RPCA-based non-linear subject-toatlas registration could provide valuable segmentation prior information . The aim of this study is to evaluate the suitability of RPCA and RPCAbased registration for ischemic stroke segmentation in follow-up FLAIR MR data sets . Building on a top-ranked segmentation approach of ISLES 2015 , the performance of RPCA sparse component image information as random forest ( RF ) feature is evaluated . The proposed method takes into account discrete nature of the outcome , yielding correct interpretation of each uncertainty . We demonstrate that the proposed uncertainty quantification method provides additional insight to the point prediction using images from the Ischemic Stroke Lesion Segmentation Challenge . The challenge of Ischemic Stroke Lesion Segmentation 2018 asks for methods that allow the segmentation of stroke lesion based on acute CT perfusion data , and provided a data set of 103 stroke patients and matching expert segmentations . In this paper , a novel deep learning framework with extractor , generator and segmentor for ischemic stroke lesion segmentation has been proposed . Algorithms for stroke lesion segmentation from magnetic resonance imaging ( MRI ) volumes are intensely researched , but the reported results are largely incomparable due to different datasets and evaluation schemes . We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation ( ISLES ) challenge organized in conjunction with the MICCAI 2015 conference . In this paper we propose a common evaluation framework , describe the publicly available datasets , and present the results of the two sub‐challenges : Sub‐Acute Stroke Lesion Segmentation ( SISS ) and Stroke Perfusion Estimation ( SPES ) . It was shown that ischemic brain stroke region is related with local low frequency wavelet coefficients and the main changes are observed at the contour of the region . It is recommended two concepts of ischemic stroke region detection , first , by grouping of wavelet coefficients with certain absolute value and detecting the contour of the region , second , by grouping small value coefficients and detecting the internal points of the region . Ill. 7 , bibl . [ b ] 1.0 subsection : Ischemic Stroke Lesion Segmentation subsubsection : Material and Pre - Processing We participated in the 2015 Ischemic Stroke Lesion Segmentation ( ISLES ) challenge , where our system achieved the best results among all participants on sub - acute ischemic stroke lesions ( ) . In the training phase of the challenge , 28 datasets have been made available , along with manual segmentations . Automatic pancreas segmentation in radiology images , e.g. , computed tomography ( CT ) , and magnetic resonance imaging ( MRI ) , is frequently required by computer-aided screening , diagnosis , and quantitative assessment . Yet , pancreas is a challenging abdominal organ to segment due to the high inter-patient anatomical variability in both shape and volume metrics . The pancreas is a highly variable organ , the size , shape , and position of which are affected by age , sex , adiposity , the presence of diseases affecting the pancreas ( e.g. , diabetes , pancreatic cancer , pancreatitis ) and other factors . Accurate automated segmentation of the pancreas has the potential to facilitate timely diagnosing and managing of diseases of the endocrine and exocrine pancreas . The aim was to systematically review studies reporting on automated pancreas segmentation algorithms derived from computed tomography ( CT ) or magnetic resonance ( MR ) images . Studies using the other two types of algorithms were not meta-analysable . The above findings indicate that the automation of pancreas segmentation represents a considerable challenge as the performance of current automated pancreas segmentation algorithms is suboptimal . Adopting standardised reporting on performance of pancreas segmentation algorithms and encouraging the use of benchmark pancreas segmentation datasets will allow future algorithms to be tested and compared more easily and fairly . DL required least human supervision and was faster to compute . Conclusions Our study demonstrated potential feasibility of automated segmentation of the pancreas on MRI scans with minimal human supervision at the beginning of imaging acquisition . The achieved accuracy is promising for organ localization . Patient-specific atlas is a key technology for the recognition of the human anatomy from 3D medical images . Automated recognition of the pancreas is one main issue for computer-assisted diagnosis and therapy systems in the abdomen because many diseases of the pancreas are not accompanied by noticeable symptoms . In patient-specific atlas generation , hierarchical and mosaicing methods have been proposed to cope with individual differences in the position , orientation , and shape of the pancreas . Motion trajectories derived from the hGReS were highly correlated to respiratory motion . CONCLUSION We have shown the feasibility of automated segmentation of the pancreas anatomy on dMRI . ADVANCES IN KNOWLEDGE Using the hybrid method improves segmentation robustness of low-contrast images . Automatic and accurate pancreas segmentation from 3D computed tomography volumes is a crucial prerequisite for computer-aided diagnosis , intraoperative planning and guidance . However , this is a challenging task because of the high inter-subject variability in the shape and location of the pancreas , as well as the existence of the surrounding organs . Meanwhile , we propose a new loss function based on the generalized dice coefficient to address the class imbalance of pancreas . Experiments showed the proposed method outperformed state-of-the-art methods for automatically segmenting pancreas CT images in terms of accuracy and reliability . Automatic and reliable segmentation of the pancreas is an important but difficult task for various clinical applications , such as pancreatic cancer radiotherapy and computer-aided diagnosis ( CAD ) . The main challenges for accurate CT pancreas segmentation lie in two aspects : ( 1 ) large shape variation across different patients , and ( 2 ) low contrast and blurring around the pancreas boundary . In this paper , we propose a two-stage , ensemble-based fully convolutional neural network ( FCN ) to solve the challenging pancreas segmentation problem in CT images . Additionally , a novel segmentation-direct loss function ( named Jaccard Loss ) is proposed and deep networks are trained to optimize Jaccard Index ( JI ) directly . Extensive experiments are conducted to validate our proposed deep models , on quantitative pancreas segmentation using both CT and MRI scans . Our method outperforms the state-of-the-art work on CT [ 11 ] and MRI pancreas segmentation [ 1 ] , respectively .
This paper introduces a contiguity-constrained algorithm with an aggregation index which uses neighbouring relations present in the data . Experiments show the behaviour of the proposed method in the case of medical image segmentation .
However , it is also possible to use linear combinations of training images and labels to augment the dataset using the recently-proposed ‘ mixup ’ algorithm . Here , we apply this algorithm for use in medical imaging segmentation . We show that it increases performance in segmentation tasks , and also offer a theoretical suggestion for the efficacy of this technique .
Medical imaging segmentation provides vital information for surgical diagnosis , and usually demands an accurate segmentation . A fully automated computed tomography image segmentation method is proposed .
Manual segmentation of brain tumor from 3D multimodal magnetic resonance images ( MRI ) is time-consuming task and leading to human errors . In this paper , two automated approaches has been developed for brain tumor segmentation to discuss which one will provide accurate segmentation that is close to the manual results . The MR feature images used for the segmentation consist of three weighted images ( enhanced T1 , proton density(PD ) and T2 ) for each axial slice through the head .
A fully automatic segmentation algorithm based on local and global consistency with robust label initialization is proposed for brain tumor segmentation in multi-parametric MR images . A tumor probability map is first computed using support vector machine ( SVM ) classification of voxel-wise features , and then aggregated with respect to the image intrinsic structures in a multi-scale manner .
Earlier detection , diagnosis and proper treatment of brain tumor are essential to prevent human death . An effective brain tumor detection and segmentation using MR image is an essential task in medical field . A number of research papers related to medical image segmentation methods are studied .
Segmentation of brain tumor MR images is the most difficult task as the tumor varies in terms of size , shape , location , and texture . In this paper , we discuss various supervised and unsupervised techniques for brain tumor detection and segmentation such as K-nearest neighbor ( K-NN ) , K-means clustering , and using morphological operators . We also review the results obtained .
Image segmentation is considered as one of the most crucial techniques for visualizing tissues in human being . In considering brain tumor image segmentation , manually with an expert , it is more likely that the errors are present in it . To automate image segmentation , we have proposed an algorithm to obtain a global thresholding value for a particular image .
Brain tumor image segmentation has been a time-consuming and laborious task for decades . Since deep learning has been applied to the image field , there have been many efforts to find automatic segmentation methods .
Computerized screening and diagnosis of brain tumor is an intensively investigated domain , especially since the first Brain Tumor Segmentation Challenge ( BraTS ) organized seven years ago . Several ensemble learning solutions have been proposed lately to the brain tumor segmentation problem . This paper presents an evaluation framework designed to test the accuracy and efficiency of ensemble learning algorithms deployed for brain tumor segmentation using the BraTS 2016 train data set .
Various evaluation parameters like Dice index , Jaccard index , Sensitivity , Specificity are calculated for all the algorithms and comparative analysis is carried out . Experimental results state that Fuzzy C-means clustering outperforms K-means and Gaussian Mixture Model algorithm for brain tumor segmentation problem .
Fully automatic brain tumor segmentation is one of the critical tasks in magnetic resonance imaging ( MRI ) images . This proposed work is aimed to develop an automatic method for brain tumor segmentation process by wavelet transformation and clustering technique . The proposed method using discrete wavelet transform ( DWT ) for pre‐ and post‐processing , fuzzy c‐means ( FCM ) for brain tissues segmentation .
Moreover , all segmented brain tumor images are reconstructed and located to three dimensions . Experimental results show that the algorithm based on Chan-Vese model can solve the problem of incomplete segmentation easily occurred in brain tumor segmentation process . At the same time , the 3D reconstruction and location of the brain tumor offer a great reference value in clinical work .
The analysis of brain MRI is an important part of patient care and decision . Hence in the proposed Clinical Support System , the brain MRI image is preprocessed using Genetic Optimized Median Filter followed by brain tumor region segmentation using Hierarchical Fuzzy Clustering Algorithm . The features of the tumor region are extracted through GLCM feature extraction method .
Brain tumor segmentation and labeling is a challenging task in medical imaging . In this paper , a novel patch based dictionary learning algorithm for automatic multi-label brain tumor segmentation is proposed .
Currently several methods exist for tumor segmentation but they all lack high accuracy . Here we present a solution for brain tumor segmenting by using deep learning . In this work , we studied different angles of brain MR images and applied different networks for segmentation .
document : Brain Tumor Segmentation with Deep Neural Networks ti In this paper , we present a fully automatic brain tumor segmentation method based on Deep Neural Networks ( DNNs ) .
These tumors ( together with their surrounding edema ) are often diffused , poorly contrasted , and extend tentacle - like structures that make them difficult to segment . Another fundamental difficulty with segmenting brain tumors is that they can appear anywhere in the brain , in almost any shape and size . Furthermore , unlike images derived from X - ray computed tomography ( CT ) scans , the scale of voxel values in MR images is not standardized .
section : Related work As noted by , the number of publications devoted to automated brain tumor segmentation has grown exponentially in the last several decades . This observation not only underlines the need for automatic brain tumor segmentation tools , but also shows that research in that area is still a work in progress . Brain tumor segmentation methods ( especially those devoted to MRI ) can be roughly divided in two categories : those based on generative models and those based on discriminative models .
By employing dense - inference with 3D CNNs , and reported computation times of a few seconds and approximately a minute respectively for the processing of a single brain scan . Even though the size of their developed networks was limited , a factor that is directly related to a network ’s representational power , their results on MS and brain tumor segmentation respectively were very promising . Performance of CNNs is significantly influenced by the strategy for extracting training samples .
[ b ] 1.0 [ b ] 1.0 subsection : Brain Tumor Segmentation subsubsection : Material and Pre - Processing
document : 3D MRI brain tumor segmentation using autoencoder regularization Automated segmentation of brain tumors from 3D magnetic resonance images ( MRIs ) is necessary for the diagnosis , monitoring , and treatment planning of the disease . Manual delineation practices require anatomical knowledge , are expensive , time consuming and can be inaccurate due to human error .
Convolutional neural networks ( CNN ) are able to learn from examples and demonstrate state - of - the - art segmentation accuracy both in 2D natural images and in 3D medical image modalities . Multimodal Brain Tumor Segmentation Challenge ( BraTS ) aims to evaluate state - of - the - art methods for the segmentation of brain tumors by providing a 3D MRI dataset with ground truth tumor segmentation labels annotated by physicians . This year , BraTS 2018 training dataset included 285 cases ( 210 HGG and 75 LGG ) , each with four 3D MRI modalities ( T1 , T1c , T2 and FLAIR ) rigidly aligned , resampled to 1x1x1 mm isotropic resolution and skull - stripped .
Our model is evaluated on the BRATS 2015 benchmark for brain tumor segmentation , where it obtains state-of-the-art results , by surpassing recently published results reported in Med Image Anal 43 : 98–111 , 2018 , Med Image Anal 36:61–78 , 2017 . We further applied the proposed Brain SegNet for ischemic stroke lesion outcome prediction , with impressive results achieved on the Ischemic Stroke Lesion Segmentation ( ISLES ) 2017 database .
This procedure can introduce misidentification issue and time consuming . In this paper , a method for automated classification of PF slices in CT brain images is investigated to sort the slice that contains PF for further study of ischemic stroke detection . This framework employs 11 features of Gray Level Co-Occurrence Matrix ( GLCM ) .
In this paper , to improve the accuracy of detection and segmentation , we modify U-Net architecture to address ischemic stroke segmentation and detection , from ISLES 2018 dataset . In this dataset , CT images ( in five modalities ) and corresponding ground truth created by combining manual annotations are provided .
Within this context , low rank-&-sparse matrix decomposition ( also known as robust PCA , RPCA ) and RPCA-based non-linear subject-toatlas registration could provide valuable segmentation prior information . The aim of this study is to evaluate the suitability of RPCA and RPCAbased registration for ischemic stroke segmentation in follow-up FLAIR MR data sets . Building on a top-ranked segmentation approach of ISLES 2015 , the performance of RPCA sparse component image information as random forest ( RF ) feature is evaluated .
The proposed method takes into account discrete nature of the outcome , yielding correct interpretation of each uncertainty . We demonstrate that the proposed uncertainty quantification method provides additional insight to the point prediction using images from the Ischemic Stroke Lesion Segmentation Challenge .
The challenge of Ischemic Stroke Lesion Segmentation 2018 asks for methods that allow the segmentation of stroke lesion based on acute CT perfusion data , and provided a data set of 103 stroke patients and matching expert segmentations . In this paper , a novel deep learning framework with extractor , generator and segmentor for ischemic stroke lesion segmentation has been proposed .
Algorithms for stroke lesion segmentation from magnetic resonance imaging ( MRI ) volumes are intensely researched , but the reported results are largely incomparable due to different datasets and evaluation schemes . We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation ( ISLES ) challenge organized in conjunction with the MICCAI 2015 conference . In this paper we propose a common evaluation framework , describe the publicly available datasets , and present the results of the two sub‐challenges : Sub‐Acute Stroke Lesion Segmentation ( SISS ) and Stroke Perfusion Estimation ( SPES ) .
It was shown that ischemic brain stroke region is related with local low frequency wavelet coefficients and the main changes are observed at the contour of the region . It is recommended two concepts of ischemic stroke region detection , first , by grouping of wavelet coefficients with certain absolute value and detecting the contour of the region , second , by grouping small value coefficients and detecting the internal points of the region . Ill. 7 , bibl .
[ b ] 1.0 subsection : Ischemic Stroke Lesion Segmentation subsubsection : Material and Pre - Processing We participated in the 2015 Ischemic Stroke Lesion Segmentation ( ISLES ) challenge , where our system achieved the best results among all participants on sub - acute ischemic stroke lesions ( ) . In the training phase of the challenge , 28 datasets have been made available , along with manual segmentations .
Automatic pancreas segmentation in radiology images , e.g. , computed tomography ( CT ) , and magnetic resonance imaging ( MRI ) , is frequently required by computer-aided screening , diagnosis , and quantitative assessment . Yet , pancreas is a challenging abdominal organ to segment due to the high inter-patient anatomical variability in both shape and volume metrics .
The pancreas is a highly variable organ , the size , shape , and position of which are affected by age , sex , adiposity , the presence of diseases affecting the pancreas ( e.g. , diabetes , pancreatic cancer , pancreatitis ) and other factors . Accurate automated segmentation of the pancreas has the potential to facilitate timely diagnosing and managing of diseases of the endocrine and exocrine pancreas . The aim was to systematically review studies reporting on automated pancreas segmentation algorithms derived from computed tomography ( CT ) or magnetic resonance ( MR ) images .
Studies using the other two types of algorithms were not meta-analysable . The above findings indicate that the automation of pancreas segmentation represents a considerable challenge as the performance of current automated pancreas segmentation algorithms is suboptimal . Adopting standardised reporting on performance of pancreas segmentation algorithms and encouraging the use of benchmark pancreas segmentation datasets will allow future algorithms to be tested and compared more easily and fairly .
DL required least human supervision and was faster to compute . Conclusions Our study demonstrated potential feasibility of automated segmentation of the pancreas on MRI scans with minimal human supervision at the beginning of imaging acquisition . The achieved accuracy is promising for organ localization .
Patient-specific atlas is a key technology for the recognition of the human anatomy from 3D medical images . Automated recognition of the pancreas is one main issue for computer-assisted diagnosis and therapy systems in the abdomen because many diseases of the pancreas are not accompanied by noticeable symptoms . In patient-specific atlas generation , hierarchical and mosaicing methods have been proposed to cope with individual differences in the position , orientation , and shape of the pancreas .
Motion trajectories derived from the hGReS were highly correlated to respiratory motion . CONCLUSION We have shown the feasibility of automated segmentation of the pancreas anatomy on dMRI . ADVANCES IN KNOWLEDGE Using the hybrid method improves segmentation robustness of low-contrast images .
Automatic and accurate pancreas segmentation from 3D computed tomography volumes is a crucial prerequisite for computer-aided diagnosis , intraoperative planning and guidance . However , this is a challenging task because of the high inter-subject variability in the shape and location of the pancreas , as well as the existence of the surrounding organs .
Meanwhile , we propose a new loss function based on the generalized dice coefficient to address the class imbalance of pancreas . Experiments showed the proposed method outperformed state-of-the-art methods for automatically segmenting pancreas CT images in terms of accuracy and reliability .
Automatic and reliable segmentation of the pancreas is an important but difficult task for various clinical applications , such as pancreatic cancer radiotherapy and computer-aided diagnosis ( CAD ) . The main challenges for accurate CT pancreas segmentation lie in two aspects : ( 1 ) large shape variation across different patients , and ( 2 ) low contrast and blurring around the pancreas boundary . In this paper , we propose a two-stage , ensemble-based fully convolutional neural network ( FCN ) to solve the challenging pancreas segmentation problem in CT images .
Additionally , a novel segmentation-direct loss function ( named Jaccard Loss ) is proposed and deep networks are trained to optimize Jaccard Index ( JI ) directly . Extensive experiments are conducted to validate our proposed deep models , on quantitative pancreas segmentation using both CT and MRI scans . Our method outperforms the state-of-the-art work on CT [ 11 ] and MRI pancreas segmentation [ 1 ] , respectively .
Contiguity-constrained hierarchical clustering for image segmentation Improving Data Augmentation for Medical Image Segmentation Automatic body segmentation from computed tomography image Tumor Extraction From Multimodal MRI Label propagation with robust initialization for brain tumor segmentation A Review on Brain Tumor Segmentation and Definition using Soft Computing Technique Performance Analysis of Supervised & Unsupervised Techniques for Brain Tumor Detection and Segmentation from MR Images Brain tumor segmentation using DE embedded OTSU method and neural network A Compound Neural Network for Brain Tumor Segmentation Brain Tumor Detection and Segmentation from Magnetic Resonance Image Data Using Ensemble Learning Methods Comparative Study of K-means, Gaussian Mixture Model, Fuzzy C-means algorithms for Brain Tumor Segmentation Fully automatic method for segmentation of brain tumor from multimodal magnetic resonance images using wavelet transformation and clustering technique Brain tumor image segmentation method based on Chan-Vese model A Clinical Support System for Brain Tumor Classification Using Soft Computing Techniques Coupled Dictionary Learning for Automatic Multi-Label Brain Tumor Segmentation in Flair MRI images Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images Brain tumor segmentation with Deep Neural Networks Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation 3D MRI brain tumor segmentation using autoencoder regularization Brain SegNet: 3D local refinement network for brain lesion segmentation Classification of Posterior Fossa CT Brain Slices using Artificial Neural Network Modified U-Net Architecture for Ischemic Stroke Lesion Segmentation and Detection Low Rank and Sparse Matrix Decomposition as Stroke Segmentation Prior: Useful or Not? A Random Forest-Based Evaluation Study Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation Integrated Extractor, Generator and Segmentor for Ischemic Stroke Lesion Segmentation ISLES 2015 ‐ A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI Wavelet Analysis of Human Brain CT Images with Ischemic Stroke Region Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning Automated pancreas segmentation from computed tomography and magnetic resonance images: A systematic review Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation Structure Specific Atlas Generation and Its Application to Pancreas Segmentation from Contrasted Abdominal CT Volumes Feasibility of automated pancreas segmentation based on dynamic MRI. Iterative 3D feature enhancement network for pancreas segmentation from CT images Fixed-Point Deformable U-Net for Pancreas CT Segmentation Automatic Pancreas Segmentation via Coarse Location and Ensemble Learning Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
c996acddd12dc95560150f3fe2bd5ec057abb23a 16a537f71dc4c674b216c715670f56d6a4d8e92a 56ee4613e47a92d5e19ac774352b34ecce1d10a8 5b3e2dc7384510facfd64e4760930e973446cefd bea4eeeb619a8f1a9197487ae89d47b3aa71fe9d 811676fb7ebd396602dd99b903799992006efbdc d81001b43212504a654d17785e9085a2db09ce71 878eae22d2619b407ee86791b8d6aacff2bc219a 3cb882776213a3ba3eb6f8ff93b3e50a6ec040d2 db2efb6e8d354c79f7bccae2b12e463afc9fbf5b db3f52771e305ae5bd69b5189cb394a533c1bf1d 0d13100c04aa3ffff8b42f939abcfb3c03d6e04e ebe40dc1ebb348f3df54436f2e0f463cd9802922 d21dca8d36ef08e62ae76f0140ec2f304a33e425 4ec408766b867c86d5b320555e6e5b18fa9a72aa d18cca21bfe966206bee2f91a9c026cb42ceee4b 6551776fbb62865ef9954928c1383c46e7de959a 8a5331e0a396bac7d438b9fc9218690951cd46d2 6aec41fe2f07e86a4de98453eaae297eb802bc67 afaf6b41b128db42eb34ac107882e6719f404205 146f8844a380191a3f883c3584df3d7a6a56a999 b1833434e9fa6d84bb4257f900779ca13ec173d9 9999184caaa7e4ca36fbc3fe9d65028fae8f2e42 00cb92b656f448bcd55a5b1d5214f345765645bc f262aa21d4ca692ad405356c07b1736fc8b10714 8decd7f26864271e857b4304da04bf1fd3abd614 b74f82b18bf30a297b949ad3cc427047b6d193b9 a0ce01753781edf49908201d0cae63d6a589b9e4 b22e613a1d6be7bf9a7299a3afcec5911c979a64 e8a431f7849d72f2f5ac7816cea4e43a559c189c 81ae04a687b0612dfa7c4b1da8a9633477b780df 3964ba947347eb59c19d4b42d7a5623e357a81d5 65147d0652741e886243549123dab142699e07eb
["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Medicine","Computer Science"] ["Engineering"] ["Computer Science"] ["Medicine","Computer Science"] ["Medicine","Computer Science"] ["Computer Science","Medicine"] ["Medicine"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"]
2010 2018 2017 2009 2012 2017 2018 2019 2019 2019 2017 2016 2010 2019 2014 2018 2017 2017 2018 2020 2018 2019 2017 2018 2018 2017 2005 2017 2019 2019 2016 2015 2014 2020 2019 2020 2017
IPTA ATSIP Computer Recognition Systems 3 ISBI ICIP SMC ISVC (1) Medical Image Anal. Medical Image Anal. BrainLes@MICCAI Bildverarbeitung f&uuml;r die Medizin BrainLes@MICCAI (1) Medical Image Anal. Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics MCV@MICCAI ISICDM
https://api.semanticscholar.org/c996acddd12dc95560150f3fe2bd5ec057abb23a https://api.semanticscholar.org/16a537f71dc4c674b216c715670f56d6a4d8e92a https://api.semanticscholar.org/56ee4613e47a92d5e19ac774352b34ecce1d10a8 https://api.semanticscholar.org/5b3e2dc7384510facfd64e4760930e973446cefd https://api.semanticscholar.org/bea4eeeb619a8f1a9197487ae89d47b3aa71fe9d https://api.semanticscholar.org/811676fb7ebd396602dd99b903799992006efbdc https://api.semanticscholar.org/d81001b43212504a654d17785e9085a2db09ce71 https://api.semanticscholar.org/878eae22d2619b407ee86791b8d6aacff2bc219a https://api.semanticscholar.org/3cb882776213a3ba3eb6f8ff93b3e50a6ec040d2 https://api.semanticscholar.org/db2efb6e8d354c79f7bccae2b12e463afc9fbf5b https://api.semanticscholar.org/db3f52771e305ae5bd69b5189cb394a533c1bf1d https://api.semanticscholar.org/0d13100c04aa3ffff8b42f939abcfb3c03d6e04e https://api.semanticscholar.org/ebe40dc1ebb348f3df54436f2e0f463cd9802922 https://api.semanticscholar.org/d21dca8d36ef08e62ae76f0140ec2f304a33e425 https://api.semanticscholar.org/4ec408766b867c86d5b320555e6e5b18fa9a72aa https://api.semanticscholar.org/d18cca21bfe966206bee2f91a9c026cb42ceee4b https://www.semanticscholar.org/paper/441ff323c92331e655ce9ff896773fc00b55089a https://www.semanticscholar.org/paper/7c2bcf6f32b05a04cd3444c030db743e5666af88 https://www.semanticscholar.org/paper/37a18be8c599b781cc28b6a62d8f11e8a6a75169 https://api.semanticscholar.org/6551776fbb62865ef9954928c1383c46e7de959a https://api.semanticscholar.org/8a5331e0a396bac7d438b9fc9218690951cd46d2 https://api.semanticscholar.org/6aec41fe2f07e86a4de98453eaae297eb802bc67 https://api.semanticscholar.org/afaf6b41b128db42eb34ac107882e6719f404205 https://api.semanticscholar.org/146f8844a380191a3f883c3584df3d7a6a56a999 https://api.semanticscholar.org/b1833434e9fa6d84bb4257f900779ca13ec173d9 https://api.semanticscholar.org/9999184caaa7e4ca36fbc3fe9d65028fae8f2e42 https://api.semanticscholar.org/00cb92b656f448bcd55a5b1d5214f345765645bc https://www.semanticscholar.org/paper/7c2bcf6f32b05a04cd3444c030db743e5666af88 https://api.semanticscholar.org/f262aa21d4ca692ad405356c07b1736fc8b10714 https://api.semanticscholar.org/8decd7f26864271e857b4304da04bf1fd3abd614 https://api.semanticscholar.org/b74f82b18bf30a297b949ad3cc427047b6d193b9 https://api.semanticscholar.org/a0ce01753781edf49908201d0cae63d6a589b9e4 https://api.semanticscholar.org/b22e613a1d6be7bf9a7299a3afcec5911c979a64 https://api.semanticscholar.org/e8a431f7849d72f2f5ac7816cea4e43a559c189c https://api.semanticscholar.org/81ae04a687b0612dfa7c4b1da8a9633477b780df https://api.semanticscholar.org/3964ba947347eb59c19d4b42d7a5623e357a81d5 https://api.semanticscholar.org/65147d0652741e886243549123dab142699e07eb