Models trained with extreme augmentation can then be used to accelerate the generation of manually labelled data . Hence , we show that extreme augmentation can be a valuable tool in addressing scaling up small imaging data sets to address medical image segmentation tasks . Abstract One of the most challenging issues that hinder the development of accurate medical image segmentation system is the insufficiency of features which are relevant to actual anatomical meaning from the images . Although deformation of normal structures caused by compression from abnormal structures has usually been considered as undesired or even a problem to be tackled in medical image segmentation tasks , it is actually relevant to the correlation between normal and pathological structures . With the objective of investigating the feasibility of extracting and applying deformation-based features of such type , we propose an approach to estimate feature from the correlation between brain lateral ventricular ( LaV ) deformation and tumor and apply the extracted feature for computerized magnetic resonance ( MR ) image tumor segmentation . We conduct ablation studies and other experiments in order to understand the behavior of each method and compare their best results . The results show improvements over training models in a supervised learning fashion and demonstrate that recent semi-supervised learning methods are promising for domain adaptation in medical imaging segmentation . The model-based technique offers a unique and efficient approach toward medical image segmentation and analysis due to its power to unify image information within a physical framework . Of the model-based techniques , the deformable model is most effectively used for its ability to unify image statistics — both local and global — in a geometrically constrained framework . In medical image analysis and segmentation , many conventional methods work very well on good quality tissue section images , but often fail when the images are not of good quality . Active contours or snakes are widely used in medical image processing applications especially for boundary detection . Unsupervised segmentation of Magnetic Resonance Images ( MRI ) especially brain MRI is an eminent role for medical and scientific research purpose . The recent trend of medical image analysis and segmentation is laid on the super voxel and spatial information within the super voxel of magnetic resonance images . We propose a new local spatial membership function inherent within the unsupervised modified spatial fuzzy c-mean method to compute the optimized cluster centers with a minimum number of iteration . Segmentation in general is useful in many different applications such as object and face detection and recognition . Particularly in medical image analysis segmentation plays a vital role in efficient processing of images . Segmentation is used to determine the volume of mass , planning of radiotherapy , and detection of artifacts in various organs . Patient-specific cardiovascular simulation has become a paradigm in cardiovascular research and is emerging as a powerful tool in basic , translational and clinical research . In this paper we discuss the recent development of a fully open-source SimVascular software package , which provides a complete pipeline from medical image data segmentation to patient-specific blood flow simulation and analysis . This package serves as a research tool for cardiovascular modeling and simulation , and has contributed to numerous advances in personalized medicine , surgical planning and medical device design . In order to process medical images effectively and quickly , a medical image processing method based on Spark is proposed in this paper . Firstly , a model for medical image segment based on improved fuzzy kernel clustering and CV model is built , then the medical images are processed in batches by using this model on Spark . Simulation experiments show that the method proposed in this paper achieves efficient and accurate segmentation . I Enhancement Fundametal Enhancement Techniques Adaptive Image Filtering Enhancement by Multiscale Nonlinear Operators Medical Image Enhancement with Hybrid Filters . II Segmentation Overview and Fundamentals of Medical Image Segmentation Image Segmentation by Fuzzy Clustering : Methods and Issues Segmentation with Neural Networks Deformable Models Shape Constraints in Deformable Models Gradient Vector Flow Deformable Models Fully Automated Hybrid Segmentation of the Brain Volumetric Segmentation Partial Volume Segmentation with Voxel Histograms . III Quantification Two-dimensional Shape and Texture Quantification Texture Analysis in Three Dimensions as a Cue to Medical Diagnosis Computational Neuroanatomy Using Shape Transformation Morphometry of Arterial Trees and Microvascular Networks Image-Based Computational Biomechanics of the Musculoskeletal System Three-Dimensional Bone Angle Quantification Database Selection and Feature Extraction for Neural Networks Quantitative Image Analysis for Estimation of Breast Cancer Risk Classification of Breast Lesions in Mammograms Quantitative Analysis of Cardiac Function Image Processing and Analysis in Tagged Cardiac MRI Image Interpolation and Resampling . Image and video SR are long - standing challenges of signal processing . SR aims at recovering a HR image or video from its LR version , and finds direct applications ranging from medical imaging to satellite imaging , as well as facilitating tasks such as face recognition . The reconstruction of HR data from a LR input is however a highly ill - posed problem that requires additional constraints to be solved . This would facilitate more effective guidance of the catheter , increase the accuracy of the ablative process , and eliminate or minimize the damage to surrounding tissue . In this study , a semiautomatic method for faithful cardiac segmentation was investigated using Analyze - a comprehensive processing software package developed at the Biomedical Imaging Resource , Mayo Clinic . This method included use of interactive segmentation based on math morphology and separation of the chambers based on morphological connections . In consequence , boundary detection can be optimized per landmark during model training . We demonstrate the success of the method for cardiac image segmentation . In particular we show that the Simulated Search improves the capture range and the accuracy of the boundary detection compared to a traditional training scheme . In this paper , the collective information about all the available cardiac image datasets are described . Hence all the approaches which are used for the Cardiac Image segmentation are reviewed . The Review on these techniques shows that the modern deep learning techniques which are used with high speed GPU ’s will deliver best result when compared with the other traditional image segmentation techniques . To effectively train such a highly compact model , we introduce a novel loss term inspired by knowledge distillation , by explicitly constraining the KL-divergence of our derived prediction distributions between modalities . We have extensively validated our approach on two multi-class segmentation problems : i ) cardiac structure segmentation , and ii ) abdominal organ segmentation . Different network settings , i.e. , 2D dilated network and 3D U-net , are utilized to investigate our method ’s general efficacy . In this paper we introduce a tag separation method for better cardiac boundary segmentation and tag tracking . Our approach is based on two observations in the cardiac tagged MR images : 1 ) the tag patterns have a regular texture ; 2 ) the cardiac images without tag patterns are piecewise smooth with sparse gradients . the two sparse representations , where optimization is directed by the Total Variation regularization scheme . While previous methods have focused on tag removal , our approach to acquiring both optimally-decomposed tag-only image and the cardiac image without tags simultaneously can be used for better tag tracking and cardiac boundary segmentation . We demonstrate the superior performance of the proposed approach through extensive experiments on large sets of cardiac tagged MR images . Increasing interest in quantification of local myocardial properties throughout the cardiac cycle from tagged MR ( tMR ) calls for treatment of the cardiac segmentation problem as a spatio-temporal task . The method presented for myocardial segmentation , uses dynamic programming to choose the optimal contour from a set of possible contours subject to maximizing a cost function . Nevertheless , it is still challenging because of limited training data , a poor signal-to-noise ratio of the echocardiographic data , and large variances across views for a joint learning . In addition , for a better interpretation of pathophysiological processes , clinical decision-making and prognosis , such cardiac anatomy segmentation and quantitative analysis of various clinical indices should ideally be performed for the data covering the full cardiac cycle . To tackle these challenges , a multiview recurrent aggregation network ( MV-RAN ) has been developed for the echocardiographic sequences segmentation with the full cardiac cycle analysis . Although many methods have been developed for cardiac segmentation and wall motion modeling , there are still many unresolved challenges . In this chapter , first we review related techniques for cardiac segmentation and modeling from medical images , mostly CMR . Then , we present a new deep learning method to handle segmentation of multiple structures in cine CMR data . Segmentation of human cardiac tissue has a great potential to provide critical clinical guidance for Radiofrequency Ablation ( RFA ) . Uncertainty in cardiac tissue segmentation is high because of the ambiguity of the subtle boundary and intra-linter-physician variations . In this paper , we proposed a deep learning framework for Optical Coherence Tomography ( OCT ) cardiac segmentation with uncertainty measurement . We apply mean - variance normalization ( MVN ) after each ReLU activation layer to help improve link prediction performance , where it compensates for noise between train and test instances by normalizing the activations to have zero mean and unit variance . MVN enables efficient learning and has been shown effective in cardiac semantic segmentation and speech recognition . During training , we apply dropout regularization throughout the architecture to mitigate overfitting , depending on the sparsity of the input graph . It can be used for CT and MRI images , and is accurate for various types of tissues . Discussed here version of the algorithm is developed especially for the purpose of the MRI brain segmentation . This method is still in the phase of development and therefore can be improved , thus final version of the algorithm can differ from the one presented here . Automatic Segmentation of brain MRI is used as a diagnostic tool in neuro medicine . Abnormal growth of brain tissues can be detected . Automatic structure segmentation in brain MRI is of great importance in modern medical research . Some methods were developed for automatic segmenting of brain MRI but failed to achieve desired accuracy . In this paper , we proposed a new patch-based approach for automatic segmentation of brain MRI using convolutional neural network ( CNN ) . Each brain MRI acquired from a small portion of public dataset is firstly divided into patches . Finally , by comparing to expert segmented reference segmentation of brain sub-regions , our results clearly indicate a better agreement between the manual reference and those automatic ones on T1 maps . In other words , our study provides an evidence for the hypothesis that compared to the conventionally used T1-weighted images , T1 maps lead to improved reliability in automatic brain MRI segmentation task . This research develops an automatic brain MRI segmentation computational tool with a wide range of neurological applications to detect brain patients ’ disease by analyzing the special clinical parameters extracted from the images and to provide patient-specific medical care , which can be especially helpful at early stages of the disease . The automatic brain MRI segmentation is performed based on modified pixel classification technique called fuzzy c-means followed by connected component labeling . Watershed algorithm and image noise reduction algorithm is widely used in image segmentation . In this paper , the two methods are combined , and for Segmentation of brain MRI Image , and obtain better results of segmentation . In this paper discusses principles of the algorithm of dictionary learning noise reduction . It employs a covariance matrix to adapt automatically the local distance metric to the different shape of the cluster and adapting the distance inducing matrix accordingly . A major challenge posed in the segmentation of MRI brain image is to take into account the uncertainty in the final localization of the feature vectors in the term of pixel value . This arises due to imprecise information and noise present in the image . However , using the Euclidean squared-norm distance as the similarity criterion makes FCM and PFCM only suitable for clustering the hyperspherically distributed data groups . The MRI brain image does not distribute hyperspherically , which means FCM and PFCM have intrinsic deficiency for the segmentation of MRI brain image . The center-free FCM could segment the non-linearly separable data . Automatic brain MRI image segmentation is a challenging problem and received significant attention in the field of medical image processing . In this paper , we present a new dynamic clustering algorithm based on the Harmony Search ( HS ) hybridized with Fuzzy C-means called DCHS to automatically segment the brain MRI image in an intelligent manner . Automatic segmentation of brain MRI data usually leaves some segmentation errors behind that are to be subsequently removed interactively using computer graphics tools . This interactive removal is normally performed by operating on individual 2D slices . In this paper , we propose a dual aggregation network to adaptively aggregate different information in infant brain MRI segmentation . More precisely , we added two modules based on 3D-UNet to better model information at different levels and locations . The classification accuracy of the proposed method is high , and the calculation is simple . And the method is able to extract the unobvious image features better , and can overcome the shortcomings of traditional methods of MRI brain area segmentation , which are time-consuming and highly complicate . The method provides an objective diagnosis approach for ADHD . document : Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain developement . A good interactive segmentation method should achieve high accuracy , minimize user interactions with low variability among users , and be computationally fast . Exploiting recent advances in machine learning , I explore a family of new interactive methods for placenta segmentation from fetal MR images . I investigate the combination of user interactions with learning from a single image or a large set of images . Different approaches are utilized to automatically segment the mother ’s soft tissue , uterus , placenta , its peripheral blood vessels , and umbilical cord from multiple ( axial , sagittal and coronal ) MRI views or a super-resolution reconstruction . In particular , 3D Gabor filters , texture features and Support Vector Machines are employed for placenta segmentation , and curvature-based corner detector for peripheral vessels detection . The mother ’s womb is localized and subsequently segmented using reinforcement learning and capsule networks . The correlation coefficient between test – retest volumes was r = 0.88 , and the intraclass correlation was ICC ( 1 ) = 0.86 . Conclusions : MALF is a promising method that can allow accurate and reliable segmentation of the placenta with minimal user interaction . Further refinement of this technique may allow for placental biometry to be incorporated into clinical pregnancy surveillance . In addition , we integrate user interactions as hard constraints into a back-propagatable Conditional Random Field . We validated the proposed framework in the context of 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images . Experimental results show our method achieves a large improvement from automatic CNNs , and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods . As such , placental segmentation efforts to date have focused on interactive methods that require considerable user input , or automated methods with relatively low performance and various limitations . We propose a novel algorithm using a combination of deep learning and multi-atlas joint label fusion ( JLF ) methods for automated segmentation of the placenta in 3DUS images . We extract 2D cross-sections of the ultrasound cone beam with a variety of orientations from the 3DUS images and train a convolutional neural network ( CNN ) on these slices . Segmentation time is reduced over manual segmentation by 2.5 and 5 times , depending on the rater . Additional experiments in interactive placenta segmentation in 3D fetal ultrasound illustrate the generalizability of the new functionality to a different problem domain . Firstly , artificial selected region of interest ( ROI ) is regarded as the object of feature extraction . Then traditional gray-scale statistical analysis is used to extract the characteristic parameters of B-placenta image as the basis data for the placenta classification . The binary tree multi-classification SVM is used to automatically classify the B-placenta image . The new model is supported by a modified image acquisition to enable successful model fitting , but this acquisition is clinically practical ; we implemented the acquisition on a standard 1.5 T clinical system with acquisition taking less than 20 minutes with 26 slices . This is particularly important for placenta image acquisition . We test this combined acquisition and model-fitting routine on simulated data and show parametric maps for a placenta dataset . Analysis of the placenta is extremely useful for evaluating health risks of the mother and baby after delivery . In this paper , we tackle the problem of automatic morphological characterization of placentas , including the tasks of placenta image segmentation , umbilical cord insertion point localization , and maternal/fetal side classification . We curated an existing dataset consisting of around 1,000 placenta images taken at Northwestern Memorial Hospital , together with their pixel-level segmentation map . Our method uses 3D US images from different views acquired with a multi-probe system . A whole placenta segmentation is obtained from these images by using a novel technique based on 3D convolutional neural networks . We demonstrate the performance of our method on 3D US images of the placenta in the last trimester .
Models trained with extreme augmentation can then be used to accelerate the generation of manually labelled data . Hence , we show that extreme augmentation can be a valuable tool in addressing scaling up small imaging data sets to address medical image segmentation tasks .
Abstract One of the most challenging issues that hinder the development of accurate medical image segmentation system is the insufficiency of features which are relevant to actual anatomical meaning from the images . Although deformation of normal structures caused by compression from abnormal structures has usually been considered as undesired or even a problem to be tackled in medical image segmentation tasks , it is actually relevant to the correlation between normal and pathological structures . With the objective of investigating the feasibility of extracting and applying deformation-based features of such type , we propose an approach to estimate feature from the correlation between brain lateral ventricular ( LaV ) deformation and tumor and apply the extracted feature for computerized magnetic resonance ( MR ) image tumor segmentation .
We conduct ablation studies and other experiments in order to understand the behavior of each method and compare their best results . The results show improvements over training models in a supervised learning fashion and demonstrate that recent semi-supervised learning methods are promising for domain adaptation in medical imaging segmentation .
The model-based technique offers a unique and efficient approach toward medical image segmentation and analysis due to its power to unify image information within a physical framework . Of the model-based techniques , the deformable model is most effectively used for its ability to unify image statistics — both local and global — in a geometrically constrained framework .
In medical image analysis and segmentation , many conventional methods work very well on good quality tissue section images , but often fail when the images are not of good quality . Active contours or snakes are widely used in medical image processing applications especially for boundary detection .
Unsupervised segmentation of Magnetic Resonance Images ( MRI ) especially brain MRI is an eminent role for medical and scientific research purpose . The recent trend of medical image analysis and segmentation is laid on the super voxel and spatial information within the super voxel of magnetic resonance images . We propose a new local spatial membership function inherent within the unsupervised modified spatial fuzzy c-mean method to compute the optimized cluster centers with a minimum number of iteration .
Segmentation in general is useful in many different applications such as object and face detection and recognition . Particularly in medical image analysis segmentation plays a vital role in efficient processing of images . Segmentation is used to determine the volume of mass , planning of radiotherapy , and detection of artifacts in various organs .
Patient-specific cardiovascular simulation has become a paradigm in cardiovascular research and is emerging as a powerful tool in basic , translational and clinical research . In this paper we discuss the recent development of a fully open-source SimVascular software package , which provides a complete pipeline from medical image data segmentation to patient-specific blood flow simulation and analysis . This package serves as a research tool for cardiovascular modeling and simulation , and has contributed to numerous advances in personalized medicine , surgical planning and medical device design .
In order to process medical images effectively and quickly , a medical image processing method based on Spark is proposed in this paper . Firstly , a model for medical image segment based on improved fuzzy kernel clustering and CV model is built , then the medical images are processed in batches by using this model on Spark . Simulation experiments show that the method proposed in this paper achieves efficient and accurate segmentation .
I Enhancement Fundametal Enhancement Techniques Adaptive Image Filtering Enhancement by Multiscale Nonlinear Operators Medical Image Enhancement with Hybrid Filters . II Segmentation Overview and Fundamentals of Medical Image Segmentation Image Segmentation by Fuzzy Clustering : Methods and Issues Segmentation with Neural Networks Deformable Models Shape Constraints in Deformable Models Gradient Vector Flow Deformable Models Fully Automated Hybrid Segmentation of the Brain Volumetric Segmentation Partial Volume Segmentation with Voxel Histograms . III Quantification Two-dimensional Shape and Texture Quantification Texture Analysis in Three Dimensions as a Cue to Medical Diagnosis Computational Neuroanatomy Using Shape Transformation Morphometry of Arterial Trees and Microvascular Networks Image-Based Computational Biomechanics of the Musculoskeletal System Three-Dimensional Bone Angle Quantification Database Selection and Feature Extraction for Neural Networks Quantitative Image Analysis for Estimation of Breast Cancer Risk Classification of Breast Lesions in Mammograms Quantitative Analysis of Cardiac Function Image Processing and Analysis in Tagged Cardiac MRI Image Interpolation and Resampling .
Image and video SR are long - standing challenges of signal processing . SR aims at recovering a HR image or video from its LR version , and finds direct applications ranging from medical imaging to satellite imaging , as well as facilitating tasks such as face recognition . The reconstruction of HR data from a LR input is however a highly ill - posed problem that requires additional constraints to be solved .
This would facilitate more effective guidance of the catheter , increase the accuracy of the ablative process , and eliminate or minimize the damage to surrounding tissue . In this study , a semiautomatic method for faithful cardiac segmentation was investigated using Analyze - a comprehensive processing software package developed at the Biomedical Imaging Resource , Mayo Clinic . This method included use of interactive segmentation based on math morphology and separation of the chambers based on morphological connections .
In consequence , boundary detection can be optimized per landmark during model training . We demonstrate the success of the method for cardiac image segmentation . In particular we show that the Simulated Search improves the capture range and the accuracy of the boundary detection compared to a traditional training scheme .
In this paper , the collective information about all the available cardiac image datasets are described . Hence all the approaches which are used for the Cardiac Image segmentation are reviewed . The Review on these techniques shows that the modern deep learning techniques which are used with high speed GPU ’s will deliver best result when compared with the other traditional image segmentation techniques .
To effectively train such a highly compact model , we introduce a novel loss term inspired by knowledge distillation , by explicitly constraining the KL-divergence of our derived prediction distributions between modalities . We have extensively validated our approach on two multi-class segmentation problems : i ) cardiac structure segmentation , and ii ) abdominal organ segmentation . Different network settings , i.e. , 2D dilated network and 3D U-net , are utilized to investigate our method ’s general efficacy .
In this paper we introduce a tag separation method for better cardiac boundary segmentation and tag tracking . Our approach is based on two observations in the cardiac tagged MR images : 1 ) the tag patterns have a regular texture ; 2 ) the cardiac images without tag patterns are piecewise smooth with sparse gradients .
the two sparse representations , where optimization is directed by the Total Variation regularization scheme . While previous methods have focused on tag removal , our approach to acquiring both optimally-decomposed tag-only image and the cardiac image without tags simultaneously can be used for better tag tracking and cardiac boundary segmentation . We demonstrate the superior performance of the proposed approach through extensive experiments on large sets of cardiac tagged MR images .
Increasing interest in quantification of local myocardial properties throughout the cardiac cycle from tagged MR ( tMR ) calls for treatment of the cardiac segmentation problem as a spatio-temporal task . The method presented for myocardial segmentation , uses dynamic programming to choose the optimal contour from a set of possible contours subject to maximizing a cost function .
Nevertheless , it is still challenging because of limited training data , a poor signal-to-noise ratio of the echocardiographic data , and large variances across views for a joint learning . In addition , for a better interpretation of pathophysiological processes , clinical decision-making and prognosis , such cardiac anatomy segmentation and quantitative analysis of various clinical indices should ideally be performed for the data covering the full cardiac cycle . To tackle these challenges , a multiview recurrent aggregation network ( MV-RAN ) has been developed for the echocardiographic sequences segmentation with the full cardiac cycle analysis .
Although many methods have been developed for cardiac segmentation and wall motion modeling , there are still many unresolved challenges . In this chapter , first we review related techniques for cardiac segmentation and modeling from medical images , mostly CMR . Then , we present a new deep learning method to handle segmentation of multiple structures in cine CMR data .
Segmentation of human cardiac tissue has a great potential to provide critical clinical guidance for Radiofrequency Ablation ( RFA ) . Uncertainty in cardiac tissue segmentation is high because of the ambiguity of the subtle boundary and intra-linter-physician variations . In this paper , we proposed a deep learning framework for Optical Coherence Tomography ( OCT ) cardiac segmentation with uncertainty measurement .
We apply mean - variance normalization ( MVN ) after each ReLU activation layer to help improve link prediction performance , where it compensates for noise between train and test instances by normalizing the activations to have zero mean and unit variance . MVN enables efficient learning and has been shown effective in cardiac semantic segmentation and speech recognition . During training , we apply dropout regularization throughout the architecture to mitigate overfitting , depending on the sparsity of the input graph .
It can be used for CT and MRI images , and is accurate for various types of tissues . Discussed here version of the algorithm is developed especially for the purpose of the MRI brain segmentation . This method is still in the phase of development and therefore can be improved , thus final version of the algorithm can differ from the one presented here .
Automatic Segmentation of brain MRI is used as a diagnostic tool in neuro medicine . Abnormal growth of brain tissues can be detected .
Automatic structure segmentation in brain MRI is of great importance in modern medical research . Some methods were developed for automatic segmenting of brain MRI but failed to achieve desired accuracy . In this paper , we proposed a new patch-based approach for automatic segmentation of brain MRI using convolutional neural network ( CNN ) . Each brain MRI acquired from a small portion of public dataset is firstly divided into patches .
Finally , by comparing to expert segmented reference segmentation of brain sub-regions , our results clearly indicate a better agreement between the manual reference and those automatic ones on T1 maps . In other words , our study provides an evidence for the hypothesis that compared to the conventionally used T1-weighted images , T1 maps lead to improved reliability in automatic brain MRI segmentation task .
This research develops an automatic brain MRI segmentation computational tool with a wide range of neurological applications to detect brain patients ’ disease by analyzing the special clinical parameters extracted from the images and to provide patient-specific medical care , which can be especially helpful at early stages of the disease . The automatic brain MRI segmentation is performed based on modified pixel classification technique called fuzzy c-means followed by connected component labeling .
Watershed algorithm and image noise reduction algorithm is widely used in image segmentation . In this paper , the two methods are combined , and for Segmentation of brain MRI Image , and obtain better results of segmentation . In this paper discusses principles of the algorithm of dictionary learning noise reduction .
It employs a covariance matrix to adapt automatically the local distance metric to the different shape of the cluster and adapting the distance inducing matrix accordingly . A major challenge posed in the segmentation of MRI brain image is to take into account the uncertainty in the final localization of the feature vectors in the term of pixel value . This arises due to imprecise information and noise present in the image .
However , using the Euclidean squared-norm distance as the similarity criterion makes FCM and PFCM only suitable for clustering the hyperspherically distributed data groups . The MRI brain image does not distribute hyperspherically , which means FCM and PFCM have intrinsic deficiency for the segmentation of MRI brain image . The center-free FCM could segment the non-linearly separable data .
Automatic brain MRI image segmentation is a challenging problem and received significant attention in the field of medical image processing . In this paper , we present a new dynamic clustering algorithm based on the Harmony Search ( HS ) hybridized with Fuzzy C-means called DCHS to automatically segment the brain MRI image in an intelligent manner .
Automatic segmentation of brain MRI data usually leaves some segmentation errors behind that are to be subsequently removed interactively using computer graphics tools . This interactive removal is normally performed by operating on individual 2D slices .
In this paper , we propose a dual aggregation network to adaptively aggregate different information in infant brain MRI segmentation . More precisely , we added two modules based on 3D-UNet to better model information at different levels and locations .
The classification accuracy of the proposed method is high , and the calculation is simple . And the method is able to extract the unobvious image features better , and can overcome the shortcomings of traditional methods of MRI brain area segmentation , which are time-consuming and highly complicate . The method provides an objective diagnosis approach for ADHD .
document : Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain developement .
A good interactive segmentation method should achieve high accuracy , minimize user interactions with low variability among users , and be computationally fast . Exploiting recent advances in machine learning , I explore a family of new interactive methods for placenta segmentation from fetal MR images . I investigate the combination of user interactions with learning from a single image or a large set of images .
Different approaches are utilized to automatically segment the mother ’s soft tissue , uterus , placenta , its peripheral blood vessels , and umbilical cord from multiple ( axial , sagittal and coronal ) MRI views or a super-resolution reconstruction . In particular , 3D Gabor filters , texture features and Support Vector Machines are employed for placenta segmentation , and curvature-based corner detector for peripheral vessels detection . The mother ’s womb is localized and subsequently segmented using reinforcement learning and capsule networks .
The correlation coefficient between test – retest volumes was r = 0.88 , and the intraclass correlation was ICC ( 1 ) = 0.86 . Conclusions : MALF is a promising method that can allow accurate and reliable segmentation of the placenta with minimal user interaction . Further refinement of this technique may allow for placental biometry to be incorporated into clinical pregnancy surveillance .
In addition , we integrate user interactions as hard constraints into a back-propagatable Conditional Random Field . We validated the proposed framework in the context of 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images . Experimental results show our method achieves a large improvement from automatic CNNs , and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods .
As such , placental segmentation efforts to date have focused on interactive methods that require considerable user input , or automated methods with relatively low performance and various limitations . We propose a novel algorithm using a combination of deep learning and multi-atlas joint label fusion ( JLF ) methods for automated segmentation of the placenta in 3DUS images . We extract 2D cross-sections of the ultrasound cone beam with a variety of orientations from the 3DUS images and train a convolutional neural network ( CNN ) on these slices .
Segmentation time is reduced over manual segmentation by 2.5 and 5 times , depending on the rater . Additional experiments in interactive placenta segmentation in 3D fetal ultrasound illustrate the generalizability of the new functionality to a different problem domain .
Firstly , artificial selected region of interest ( ROI ) is regarded as the object of feature extraction . Then traditional gray-scale statistical analysis is used to extract the characteristic parameters of B-placenta image as the basis data for the placenta classification . The binary tree multi-classification SVM is used to automatically classify the B-placenta image .
The new model is supported by a modified image acquisition to enable successful model fitting , but this acquisition is clinically practical ; we implemented the acquisition on a standard 1.5 T clinical system with acquisition taking less than 20 minutes with 26 slices . This is particularly important for placenta image acquisition . We test this combined acquisition and model-fitting routine on simulated data and show parametric maps for a placenta dataset .
Analysis of the placenta is extremely useful for evaluating health risks of the mother and baby after delivery . In this paper , we tackle the problem of automatic morphological characterization of placentas , including the tasks of placenta image segmentation , umbilical cord insertion point localization , and maternal/fetal side classification . We curated an existing dataset consisting of around 1,000 placenta images taken at Northwestern Memorial Hospital , together with their pixel-level segmentation map .
Our method uses 3D US images from different views acquired with a multi-probe system . A whole placenta segmentation is obtained from these images by using a novel technique based on 3D convolutional neural networks . We demonstrate the performance of our method on 3D US images of the placenta in the last trimester .
Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan? Extraction and application of deformation-based feature in medical images Semi-supervised learning methods for unsupervised domain adaptation in medical imaging segmentation Parametric Contour Model In Medical Image Segmentation Cell boundary analysis using radial search for dual staining techniques An unsupervised modified spatial fuzzy C-mean method for segmentation of brain MR image Comprehensive Review of Automatic Lung Segmentation Techniques on Pulmonary CT Images SimVascular: An Open Source Pipeline for Cardiovascular Simulation Spark Distributed Medical Image Segmentation Method Based on KFCM-CV Model Handbook of Medical Imaging. Processing and Analysis Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation Semiautomatic segmentation of the heart from CT images based on intensity and morphological features Optimizing boundary detection via Simulated Search with applications to multi-modal heart segmentation A Review on Cardiac Image Segmentation Unpaired Multi-Modal Segmentation via Knowledge Distillation Tag Separation in Cardiac Tagged MRI Segmentation and Tracking of Myocardial Boundaries Using Dynamic Programming MV-RAN: Multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis Deformable models, sparsity and learning-based segmentation for cardiac MRI based analytics Segmentation and Uncertainty Measures of Cardiac Substrates within Optical Coherence Tomography Images via Convolutional Neural Networks Learning to Make Predictions on Graphs with Autoencoders MRI Brain Segmentation Using Cellular Automaton Approach Neural network model for Automatic Segmentation of brain MRI Brain MRI segmentation with patch-based CNN approach Brain segmentation performance using T1-weighted images versus T1 maps A Computational Segmentation Tool for Processing Patient Brain MRI Image Data to Automatically Extract Gray and White Matter Regions Based on MRI Image of Brain of Adaptive Dictionary Learning Noise Reduction to Improve Watershed Algorithm of Accurate Segmentation Research Intuitionistic Gustafson-Kessel Algorithm for Segmentation of MRI Brian Image Center-free PFCM for MRI brain image segmentation A hybrid Harmony Search algorithm to MRI brain segmentation 3D Visualization and Segmentation of Brain MRI Data Infant brain MRI segmentation with dilated convolution pyramid downsampling and self-attention [Study of attention deficit/hyperactivity disorder classification based on convolutional neural networks]. Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation Minimally interactive segmentation with application to human placenta in fetal MR images Deep learning -based segmentation methods for computer-assisted fetal surgery Minimally interactive placenta segmentation from three-dimensional ultrasound images DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation Combining Deep Learning and Multi-atlas Label Fusion for Automated Placenta Segmentation from 3DUS User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP Application of Multi-Classification Support Vector Machine in the B-Placenta Image Classification Placental Image Analysis using Coupled Diffusion-Weighted and Multi-Echo T2 MRI and a Multi-Compartment Model PlacentaNet: Automatic Morphological Characterization of Placenta Photos with Deep Learning Towards Whole Placenta Segmentation at Late Gestation Using Multi-view Ultrasound Images
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Medical Imaging: Image Processing ICACCI 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Medical Imaging: Image Processing MICCAI (2) STACOM@MICCAI ISBI 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) ICCVG (2) Medical Imaging: Image Processing SocProS (2) ICIP IEEE ICCI GRAPP Comput. Medical Imaging Graph. DATRA/PIPPI@MICCAI MICCAI (1) MICCAI (5)
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