We have studied on a multi-organ regions segmentation method from contrast-enhanced abdominal CT volume using 3D U-Net . Since non-contrast CT volumes are also usually used in the medical field , segmentation of multi-organ regions from non-contrast CT volume is also important for the medical image analysis system . In this study , we extract multi-organ regions from non-contrast CT volume using 3D U-Net and a small number of training data . The invention discloses a medical image information marking method and device and a medical device . The medical image information marking method includes the steps of distributing a unique examination serial number to each patient on whom the medical image examination is conducted , and marking medical images of the patients with the examination serial numbers . According to the technical scheme , the identifiability of image data of the patients can be improved . Machine learning has been used to classify lesions into certain classes ( e.g. , abnormal or normal , lesions or non-lesions , and malignant or benign ) in CAD . Recently , as available computational power increased dramatically , pixel/voxel-based machine learning ( PML ) has emerged in medical image processing/analysis , which uses pixel/voxel values in local regions ( or patches ) in images instead of features calculated from segmented regions as input information ; thus , feature calculation or segmentation is not required . Because PML can avoid errors caused by inaccurate feature calculation and segmentation , the performance of PML can potentially be higher than that of common classifiers . One of primary challenges in the medical image data analysis is the ability to handle abnormal , irregular and/or partial cases . In this paper , we present two different robust algorithms towards the goal of automatic planar primitive detection in 3D volumes . Electronics and Communication Engineering , Geethanjali Institute of Science and Technology , Nellore , Abstract : Segmentation aim at partitioning area in the image it will be based on the shape , color or texture . It is useful in many computer vision applications such as medical image analyzing , object detection and recognizing , forensic applications.content based image retrieval is used on the online clothing purchasing . Here our goal is to segment the clothing parts from the image on the background and the skin area . Such descriptions will further allow creating a semantically-oriented representation of visual data describing pattern features which are useful in medical decisions and computer-aided diagnostic systems . This publication presents the cognitive systems of medical data image analysis . Systems that conduct cognitive analyses are designed so that semantic analyses can be used to interpret data and reconstruct medical images . subsection : Matrix Square Root and Forward Propagation Square roots of matrices , particularly covariance matrices which are symmetric positive ( semi ) definite ( SPD ) , find applications in a variety of fields including computer vision , medical imaging and chemical physics . It is well - known any SPD matrix has a unique square root which can be computed accurately by EIG or SVD . Results reported on the 2013 BRATS test dataset reveal that our architecture improves over the currently published state - of - the - art while being over 30 times faster . [ t1 ] Accepted in Medical Image Analysis . [ fn1 ] mohammad.havaei@gmail.com Although CNNs first appeared over two decades ago , they have recently become a mainstay of the computer vision community due to their record - shattering performance in the ImageNet Large - Scale Visual Recognition Challenge . While CNNs have also been successfully applied to segmentation problems , most of the previous work has focused on non - medical tasks and many involve architectures that are not well suited to medical imagery or brain tumor segmentation in particular . Our preliminary work on using convolutional neural networks for brain tumor segmentation together with two other methods using CNNs was presented in BRATS‘14 workshop . Like our work , other recent work has exploited convolution operations in the final layer of a network to extend traditional CNN architectures for semantic scene segmentation . In the medical imaging domain in general there has been comparatively less work using CNNs for segmentation . However , some notable recent work by has used CNNs to predict the boundaries of neural tissue in electron microscopy images . document : V - Net : Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Convolutional Neural Networks ( CNNs ) have been recently employed to solve problems from both the computer vision and medical image analysis fields . Despite their popularity , most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes . Prostate segmentation is nevertheless an important task having clinical relevance both during diagnosis , where the volume of the prostate needs to be assessed , and during treatment planning , where the estimate of the anatomical boundary needs to be accurate . CNNs have been recently used for medical image segmentation . Early approaches obtain anatomy delineation in images or volumes by performing patch - wise image classification . In order to capture and better understand the complexity of brain pathologies it is important to conduct large studies with many subjects to gain the statistical power for drawing conclusions across a whole patient population . The development of accurate , automatic segmentation algorithms has therefore become a major research focus in medical image computing with the potential to offer objective , reproducible , and scalable approaches to quantitative assessment of brain lesions . Figure [ reference ] illustrates some of the challenges that arise when devising a computational approach for the task of automatic lesion segmentation . Another variant of FCN was also proposed which is called SegNet [ reference ] . Due to the great success of DCNNs in the field of computer vision , different variants of this approach are applied in different modalities of medical imaging including segmentation , classification , detection , registration , and medical information processing . The medical imaging comes from different imaging techniques such as Computer Tomography ( CT ) , ultrasound , X - ray , and Magnetic Resonance Imaging ( MRI ) . Two advanced techniques including cross - entropy loss and dice similarity are introduced for efficient training of classification and segmentation tasks in [ reference ][ reference ] . Furthermore , in medical image processing , global localization and context modulation is very often applied for localization tasks . Each pixel is assigned a class label with a desired boundary that is related to the contour of the target lesion in identification tasks . [ reference ] . As compared to SegNet , U - Net ( proposed for the medical imaging community ) does not reuse pooling indices but instead transfers the entire feature map ( at the cost of more memory ) to the corresponding decoders and concatenates them to upsampled ( via deconvolution ) decoder feature maps . There is no conv5 and max - pool 5 block in U - Net as in the VGG net architecture . Image segmentation is one of the significant computational applications of the biomedical field . Automated computational methodologies are highly preferred for medical image segmentation since these techniques are immune to human perception error . Artificial intelligence (AI)-based techniques are often used for this process since they are superior to other automated techniques in terms of accuracy and convergence time period . BACKGROUND Segmentation of anatomical structures of the heart from cardiac magnetic resonance images ( MRI ) has a significant impact on the quantitative analysis of the cardiac contractile function . Although deep convolutional neural networks ( ConvNets ) have achieved considerable success in medical imaging segmentation , it is still a challenging task for existing deep ConvNets to precisely and automatically segment multiple heart structures from cardiac MRI . This paper presents a novel recurrent interleaved attention network ( RIANet ) to comprehensively tackle this issue . 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 . Individualized and accurate segmentations of the prostate are essential for diagnosis as well as therapy planning in prostate cancer ( PCa ) . Most of the previously proposed prostate segmentation approaches rely purely on axial MRI scans , which suffer from low out-of-plane resolution . We propose a method that makes use of sagittal and coronal MRI scans to improve the accuracy of segmentation . Our scheme is fully-automated and comprises ( a ) prostate segmentation , ( b ) multimodal image registration , and ( c ) data representation and multi-classifier modules for information fusion . Following prostate boundary segmentation via an improved active shape model , the DCE/T2-w protocols and the T2-w/ex vivo histological prostatectomy specimens are brought into alignment via a deformable , multi-attribute registration scheme . T2-w/histology alignment allows for the mapping of true CaP extent onto the in vivo MRI , which is used for training and evaluation of a multi-protocol MRI CaP classifier . Segmentation of prostate boundaries in transrectal ultrasound ( TRUS ) images plays a great role in early detection of prostate cancer . Due to the low signal to noise ratio and existence of the speckle noise in TRUS images , prostate image segmentation has proven to be an extremely difficult task . This paper introduces a new fully automatic model-based prostate boundary segmentation method based on normalized cross-correlation ( NCC ) . Methods : A supervoxel is a set of pixels that have similar intensities , locations , and textures in a 3D image volume . The prostate segmentation problem is considered as assigning a binary label to each supervoxel , which is either the prostate or background . A supervoxel‐based energy function with data and smoothness terms is used to model the label . Differentimage modalities are often combined . Some examplemedical image analysis projects from our BiomedicalImaging Group at the Commonwealth Scientiﬁc andIndustrial Research Organisation ( CSIRO ) , located at theRoyal Brisbane and Women ’s Hospital , range from auto-matic knee cartilage thickness estimation from MRI [1];early detection of Alzheimer ’s disease using imaging bio-markers [ 2 ] ; early prediction of treatment response inadvanced gliomas using PET [ 3 ] , quantitative analysis ofbrain white matter using diffusion MRI [ 4 ] ; automaticprostate segmentation [ 5 ] , and automatic quantiﬁcation ofspine intervertebral disc degeneration from MRI [6].The three main components of medical image analysisare image registration , segmentation ( or auto-contouring)and image reconstruction . Reconstructed images may alsorequire pre-processing , such as noise reduction , artefactremoval ( such as metal in CT , or correcting intensityinhomogeneity in MRI ) , or correcting CBCT intensities toenable CBCT-based dose calculations ( by segmentation oftissues , or deformable registration ) . To address these limitations , we investigated a novel ASM-based method by incorporating deep feature learning into our previous liver segmentation framework . First , an adaptive feature learning probability boosting tree ( AFL-PBT ) based on both simple handcrafted features and deep learned features was developed for prostate pre-segmentation and for further shape model initialization . The proposed AFL-PBT classifier also provided a boundary searching band , which made the ASM less sensitive to model initialization . In addition , important features for development of computer aided detection systems for prostate cancer screening MRI are identified . A fast , robust prostate segmentation routine is used to segment the prostate , based on coupled appearance and anatomy models . Subsequently a voxel classification is performed using a support vector machine to compute an abnormality likelihood map of the prostate . The motivation of this study is to investigate the feasibility of developing a deep learning based computer-aided detection ( CAD ) scheme for prostate cancer detection from multi-parametric magnetic resonance images ( mpMRIs ) . The proposed scheme consists of a prostate segmentation stage and a tumor detection stage . In the first stage , we adopt a state-of-art fully convolutional network ( FCN ) architecture with residual connections to segment prostate areas from mpMRIs including T2-weighted ( T2W ) , T1 and diffusion-weighted imaging ( DWI ) . Results are presented for situations when intensity only registration performs best , when intensity plus equally weighted surface registration performs best , and when intensity plus uncertainty weighted surface registration performs best . The algorithm has been applied to register CBCT and FBCT prostate images where the uncertainty of the prostate surface segmentation was estimated using contours drawn by five experts . Moreover , MRI volumes are often affected by artefacts and distortions due to field inhomogeneity . Prostate segmentation is nevertheless an important task having clinical relevance both during diagnosis , where the volume of the prostate needs to be assessed , and during treatment planning , where the estimate of the anatomical boundary needs to be accurate . CNNs have been recently used for medical image segmentation . Automatic segmentation of organs is a crucial task for computer-aided medical image analysis . Due to the specificity of location and morphology of the pancreas , it is challenging to segment pancreas from other surrounding abdominal organs . This includes four atlas-based approaches , specifically median of atlas images ( A-Median ) [ 1 ] , atlas-based voxel-wise weighting ( A-VW ) [ 2 ] , bone enhanced atlas-based voxel-wise weighting ( A-Bone ) [ 3 ] , iterative atlas-based voxel-wise weighting ( A-Iter ) [ 4 ] , and a method based on deep learning convolutional neural network ( DL-CNN ) [ 5 ] . Automatic organ delineation was performed for bladder , rectum and bone . Overall , A-VW , A-Bone , A-Iter and A-VWexhibited comparable performance while DL-CNN showed slightly better segmentation performance resulting in Dice metrics of 0.93 , 0.90 , and 0.93 , respectively . Some of the current interests are the automatic diagnosis of liver , spleen , and kidney pathologies and the 3D volume rendering of the abdominal organs . The first and fundamental step in all these studies is the automatic organs segmentation , that is still an open problem . In this paper we propose our fully automatic system that employs a hierarchical gray level based framework to segment heart , bones ( i.e. ribs and spine ) , liver and its blood vessels , kidneys , and spleen . Its treatment can be helped by automatic detection of structures of interest in CT-scans . The automatic delimitation of organs close to the liver , of the liver itself , and of its vessels and tumors is a complex task . The aim of this work is to explore the tools of mathematical morphology , and to study how these tools can be used to improve the segmentation . In particular , our work focuses on the segmentation of mitochondria in Serial Section Electron Microscopy images of the brain . Segmentation is a highly relevant task in medical image analysis , as automatic delineation of organs and structures of interest is often necessary to perform computer assisted diagnosis . All the experiments performed make use of the public Electron Microscopy Dataset , a representation of a section taken from the CA1 hippocampus region of the brain . The automatic delineation of the boundaries of organs and other anatomical structures is a key component of many medical image processing systems . In this paper we present a generic learning approach based on a novel space of segmentation features , which can be trained to predict the overlap error and Dice coefficient of an arbitrary organ segmentation without knowing the ground truth delineation . The atlas-based techniques can be problematic for complicated structures with anatomical variability . Deformable models are among the most popular methods for automatic detection of different organs in MR images . However they still have an important limitation which is that they are sensitive to initial position and shape of the model . This paper describes the approach for automatic identifying organs from a medical CT imagery . Main assumption of this approach is the use of data sets and domain knowledge . 3 ) We use a novel approach to track the growing region changes across the CT image sequence in detecting region of interest , given a seed point as our input . We used quantitative and qualitative analysis to measure the accuracy against the given ground truth and our results presented a better performance than other generic approaches for automatic region of interest detection of organs in abdominal CT images . With the results presented in this research work , our proposed novel sequence approach method has been proven to be superior in terms of accuracy , automation and robustness . Analysis of Wireless Capsule Endoscopy ( CE ) images has become a very active area of research since this novel technology enabled access to previously inaccessible areas of the gastrointestinal tract , especially the small intestine . Art has investigated automatic segmentation of organ boundaries , detection of lesions and bleeding as well as other supervised and unsupervised analysis . However , all of this art has focused on treating the images as individual and independent observations that contribute towards a unique and separate decision . Segmentation is a highly relevant task in medical image analysis . Automatic delineation of organs and structures of interest is often necessary to perform tasks such as visual augmentation , computer assisted diagnosis , interventions and extraction of quantitative indices from images . In particular , since diagnostic and interventional imagery often consists of 3D images , being able to perform volumetric segmentations by taking into account the whole volume content at once , has a particular relevance . : Recently , there has been an increasing trend to propose computer aided diagnosis systems for biomedical pattern recognition . A computer aided diagnosis method , which aims higher classification accuracy , is developed to classify the biomedical dataset . Convolutional neural networks ( CNNs ) trained on natural images are extremely successful in image classification and localization due to superior automated feature extraction capability . In extending their use to biomedical recognition tasks , it is important to note that visual features of medical images tend to be uniquely different than natural images . There are advantages offered through training these networks on large scale medical common modality image collections pertaining to the recognition task . In this paper , we present a biomedical name recognition system , called PowerBioNE . In order to deal with the special phenomena in the biomedical domain , various evidential features are proposed and integrated through a mutual information independence model ( MIIM ) . Digital image processing and recognition were strongly developed on powerful workstations , however these procedures can also be implemented in PCs with DSP microprocessor cards or in situations where digital transforms used for image processing can be simply implemented and do not consume a lot of time . The example of biomedical image recognition performed in an optical way , by using a diffractometer , and in a digital system with a CCD camera will be described here . In order to solve the problem in the image process researches , it is very important to find an efficient , biologic recognition related , and high-level semantic image classification method . By adopting the theories of biologic visual recognition and knowledge engineering combined with biologic characteristic(information , ) and using minimum spanning tree as an image information construction tree , this paper pre-(sents ) a sparse coding-based image semantic classifier ( SCISC ) model . The experimental result shows that this model has high accuracy in image classification . There is a significant increase in the use of biomedical images in clinical medicine , disease research , and education . While the literature lists several successful methods that were developed and implemented for content-based image retrieval and recognition , they have been unable to make significant inroads in biomedical image recognition domain . The use of computer-aided diagnosis has been increasing . We apply the wavelet-based image enhancement technique developed in our earlier work [ Y. Wang et al. , Proc . ICASSP 2001 , Salt Lake City , May , 2001 ] , to a well-known biomedical pattern recognition problem : chromosome classification . Experiments were conducted on a test set of chromosome images before and after the enhancement , using the same feature measurement and classifier methods . Fractals are geometric objects that have a noninteger fractal dimension ( FD ) . The FD has been exploited for various biomedical recognition applications such as breast tumor and lung tumor detection . Our previous work shows that the FD is useful in the detection of brain tumors when a reference nontumor image is available . The issues of features informativity evaluation in biomedical signals portraits according to formal comparison of their probability values , obtained during training of supervised learning recognition systems are being considered . The purpose of this work is to increase the effectiveness of the biomedical signals recognition in diagnostic systems with supervised learning , by choosing rational structure of portraits based on the nature of their elements influence on the quality of pattern recognition . Details . As with many others types of cancer , early detection and treatment greatly increases the patient 's chance of survival . MRI prostate segmentation allows clinical personnel to design an accurate treatment plan . A novel method for MRI prostate imagery segmentation is proposed in this paper . Computer-aided diagnosis and decision-making systems have shown great effectiveness in assisting doctors for this purpose . At the same time , deep learning based on Generative Adversarial Networks can be applied to the segmentation of prostate MR images . In this paper , we propose a new computer-aided diagnosis and decision-making system based on a deep learning model to automatically segment the prostate region from prostate MR images . On MICCAI PROMISE12 test data sets , the proposed method yielded a mean Dice score of 84 % with a standard deviation of 4 % . The experimental results demonstrated that the proposed method outperformed other ASM-based prostate MRI segmentation methods and achieved a level of accuracy comparable to that of state-of-the-art methods . Efficient computer-assisted segmentation approaches are not yet able to address these issues , but rather have the potential to do so . In this paper , a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means ( FCM ) clustering algorithm , which enables multispectral T1-weighted ( T1w ) and T2-weighted ( T2w ) MRI anatomical data processing , is proposed . This approach , using an unsupervised Machine Learning technique , helps to segment the prostate gland effectively . In 12 cases , including prostates with cancer and benign prostatic hypertrophy , the mean Hausdorff distance and overlap ratio were 9.94 mm and 0.83 , respectively . ConclusionThis new automatic prostate MRI segmentation method produces satisfactory results , even at prostate ’s base and apex . The method is computationally feasible and efficient . As a result , this kind of approaches faces many difficulties when segmenting the base and apex of the prostate due to the limited slice boundary information . To tackle this problem , in this paper , we propose a deep neural network with bidirectional convolutional recurrent layers for MRI prostate image segmentation . In addition to utilizing the intraslice contexts and features , the proposed model also treats prostate slices as a data sequence and utilizes the interslice contexts to assist segmentation . However , success with prostate cancer localization based on current imaging methods has been limited due to overlap in feature space of benign and malignant tissues using any one MRI method and the interobserver variability . In this paper , we present a new unsupervised segmentation method for prostate cancer detection , using fuzzy Markov random fields ( fuzzy MRFs ) for the segmentation of multispectral MR prostate images . Typically , both hard and fuzzy MRF models have two groups of parameters to be estimated : the MRF parameters and class parameters for each pixel in the image . ( Segmentation of Prostate MRI using Probabilistic Atlas and Deformable Model ) . Le principal objectif de cette these est le developpement d'outils permettant le contourage automatique de la prostate en IRM . NVFP is incorporated into a model based method for image segmentation . The performance of our proposed method was demonstrated by segmenting prostate MR images . The segmentation results indicated that our method can achieve better performance compared with other existing methods . section : Conclusion We presented and approach based on a volumetric convolutional neural network that performs segmentation of MRI prostate volumes in a fast and accurate manner . We introduced a novel objective function that we optimise during training based on the Dice overlap coefficient between the predicted segmentation and the ground truth annotation .
We have studied on a multi-organ regions segmentation method from contrast-enhanced abdominal CT volume using 3D U-Net . Since non-contrast CT volumes are also usually used in the medical field , segmentation of multi-organ regions from non-contrast CT volume is also important for the medical image analysis system . In this study , we extract multi-organ regions from non-contrast CT volume using 3D U-Net and a small number of training data .
The invention discloses a medical image information marking method and device and a medical device . The medical image information marking method includes the steps of distributing a unique examination serial number to each patient on whom the medical image examination is conducted , and marking medical images of the patients with the examination serial numbers . According to the technical scheme , the identifiability of image data of the patients can be improved .
Machine learning has been used to classify lesions into certain classes ( e.g. , abnormal or normal , lesions or non-lesions , and malignant or benign ) in CAD . Recently , as available computational power increased dramatically , pixel/voxel-based machine learning ( PML ) has emerged in medical image processing/analysis , which uses pixel/voxel values in local regions ( or patches ) in images instead of features calculated from segmented regions as input information ; thus , feature calculation or segmentation is not required . Because PML can avoid errors caused by inaccurate feature calculation and segmentation , the performance of PML can potentially be higher than that of common classifiers .
One of primary challenges in the medical image data analysis is the ability to handle abnormal , irregular and/or partial cases . In this paper , we present two different robust algorithms towards the goal of automatic planar primitive detection in 3D volumes .
Electronics and Communication Engineering , Geethanjali Institute of Science and Technology , Nellore , Abstract : Segmentation aim at partitioning area in the image it will be based on the shape , color or texture . It is useful in many computer vision applications such as medical image analyzing , object detection and recognizing , forensic applications.content based image retrieval is used on the online clothing purchasing . Here our goal is to segment the clothing parts from the image on the background and the skin area .
Such descriptions will further allow creating a semantically-oriented representation of visual data describing pattern features which are useful in medical decisions and computer-aided diagnostic systems . This publication presents the cognitive systems of medical data image analysis . Systems that conduct cognitive analyses are designed so that semantic analyses can be used to interpret data and reconstruct medical images .
subsection : Matrix Square Root and Forward Propagation Square roots of matrices , particularly covariance matrices which are symmetric positive ( semi ) definite ( SPD ) , find applications in a variety of fields including computer vision , medical imaging and chemical physics . It is well - known any SPD matrix has a unique square root which can be computed accurately by EIG or SVD .
Results reported on the 2013 BRATS test dataset reveal that our architecture improves over the currently published state - of - the - art while being over 30 times faster . [ t1 ] Accepted in Medical Image Analysis . [ fn1 ] mohammad.havaei@gmail.com
Although CNNs first appeared over two decades ago , they have recently become a mainstay of the computer vision community due to their record - shattering performance in the ImageNet Large - Scale Visual Recognition Challenge . While CNNs have also been successfully applied to segmentation problems , most of the previous work has focused on non - medical tasks and many involve architectures that are not well suited to medical imagery or brain tumor segmentation in particular . Our preliminary work on using convolutional neural networks for brain tumor segmentation together with two other methods using CNNs was presented in BRATS‘14 workshop .
Like our work , other recent work has exploited convolution operations in the final layer of a network to extend traditional CNN architectures for semantic scene segmentation . In the medical imaging domain in general there has been comparatively less work using CNNs for segmentation . However , some notable recent work by has used CNNs to predict the boundaries of neural tissue in electron microscopy images .
document : V - Net : Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Convolutional Neural Networks ( CNNs ) have been recently employed to solve problems from both the computer vision and medical image analysis fields . Despite their popularity , most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes .
Prostate segmentation is nevertheless an important task having clinical relevance both during diagnosis , where the volume of the prostate needs to be assessed , and during treatment planning , where the estimate of the anatomical boundary needs to be accurate . CNNs have been recently used for medical image segmentation . Early approaches obtain anatomy delineation in images or volumes by performing patch - wise image classification .
In order to capture and better understand the complexity of brain pathologies it is important to conduct large studies with many subjects to gain the statistical power for drawing conclusions across a whole patient population . The development of accurate , automatic segmentation algorithms has therefore become a major research focus in medical image computing with the potential to offer objective , reproducible , and scalable approaches to quantitative assessment of brain lesions . Figure [ reference ] illustrates some of the challenges that arise when devising a computational approach for the task of automatic lesion segmentation .
Another variant of FCN was also proposed which is called SegNet [ reference ] . Due to the great success of DCNNs in the field of computer vision , different variants of this approach are applied in different modalities of medical imaging including segmentation , classification , detection , registration , and medical information processing . The medical imaging comes from different imaging techniques such as Computer Tomography ( CT ) , ultrasound , X - ray , and Magnetic Resonance Imaging ( MRI ) .
Two advanced techniques including cross - entropy loss and dice similarity are introduced for efficient training of classification and segmentation tasks in [ reference ][ reference ] . Furthermore , in medical image processing , global localization and context modulation is very often applied for localization tasks . Each pixel is assigned a class label with a desired boundary that is related to the contour of the target lesion in identification tasks .
[ reference ] . As compared to SegNet , U - Net ( proposed for the medical imaging community ) does not reuse pooling indices but instead transfers the entire feature map ( at the cost of more memory ) to the corresponding decoders and concatenates them to upsampled ( via deconvolution ) decoder feature maps . There is no conv5 and max - pool 5 block in U - Net as in the VGG net architecture .
Image segmentation is one of the significant computational applications of the biomedical field . Automated computational methodologies are highly preferred for medical image segmentation since these techniques are immune to human perception error . Artificial intelligence (AI)-based techniques are often used for this process since they are superior to other automated techniques in terms of accuracy and convergence time period .
BACKGROUND Segmentation of anatomical structures of the heart from cardiac magnetic resonance images ( MRI ) has a significant impact on the quantitative analysis of the cardiac contractile function . Although deep convolutional neural networks ( ConvNets ) have achieved considerable success in medical imaging segmentation , it is still a challenging task for existing deep ConvNets to precisely and automatically segment multiple heart structures from cardiac MRI . This paper presents a novel recurrent interleaved attention network ( RIANet ) to comprehensively tackle this issue .
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 .
Individualized and accurate segmentations of the prostate are essential for diagnosis as well as therapy planning in prostate cancer ( PCa ) . Most of the previously proposed prostate segmentation approaches rely purely on axial MRI scans , which suffer from low out-of-plane resolution . We propose a method that makes use of sagittal and coronal MRI scans to improve the accuracy of segmentation .
Our scheme is fully-automated and comprises ( a ) prostate segmentation , ( b ) multimodal image registration , and ( c ) data representation and multi-classifier modules for information fusion . Following prostate boundary segmentation via an improved active shape model , the DCE/T2-w protocols and the T2-w/ex vivo histological prostatectomy specimens are brought into alignment via a deformable , multi-attribute registration scheme . T2-w/histology alignment allows for the mapping of true CaP extent onto the in vivo MRI , which is used for training and evaluation of a multi-protocol MRI CaP classifier .
Segmentation of prostate boundaries in transrectal ultrasound ( TRUS ) images plays a great role in early detection of prostate cancer . Due to the low signal to noise ratio and existence of the speckle noise in TRUS images , prostate image segmentation has proven to be an extremely difficult task . This paper introduces a new fully automatic model-based prostate boundary segmentation method based on normalized cross-correlation ( NCC ) .
Methods : A supervoxel is a set of pixels that have similar intensities , locations , and textures in a 3D image volume . The prostate segmentation problem is considered as assigning a binary label to each supervoxel , which is either the prostate or background . A supervoxel‐based energy function with data and smoothness terms is used to model the label .
Differentimage modalities are often combined . Some examplemedical image analysis projects from our BiomedicalImaging Group at the Commonwealth Scientiﬁc andIndustrial Research Organisation ( CSIRO ) , located at theRoyal Brisbane and Women ’s Hospital , range from auto-matic knee cartilage thickness estimation from MRI [1];early detection of Alzheimer ’s disease using imaging bio-markers [ 2 ] ; early prediction of treatment response inadvanced gliomas using PET [ 3 ] , quantitative analysis ofbrain white matter using diffusion MRI [ 4 ] ; automaticprostate segmentation [ 5 ] , and automatic quantiﬁcation ofspine intervertebral disc degeneration from MRI [6].The three main components of medical image analysisare image registration , segmentation ( or auto-contouring)and image reconstruction . Reconstructed images may alsorequire pre-processing , such as noise reduction , artefactremoval ( such as metal in CT , or correcting intensityinhomogeneity in MRI ) , or correcting CBCT intensities toenable CBCT-based dose calculations ( by segmentation oftissues , or deformable registration ) .
To address these limitations , we investigated a novel ASM-based method by incorporating deep feature learning into our previous liver segmentation framework . First , an adaptive feature learning probability boosting tree ( AFL-PBT ) based on both simple handcrafted features and deep learned features was developed for prostate pre-segmentation and for further shape model initialization . The proposed AFL-PBT classifier also provided a boundary searching band , which made the ASM less sensitive to model initialization .
In addition , important features for development of computer aided detection systems for prostate cancer screening MRI are identified . A fast , robust prostate segmentation routine is used to segment the prostate , based on coupled appearance and anatomy models . Subsequently a voxel classification is performed using a support vector machine to compute an abnormality likelihood map of the prostate .
The motivation of this study is to investigate the feasibility of developing a deep learning based computer-aided detection ( CAD ) scheme for prostate cancer detection from multi-parametric magnetic resonance images ( mpMRIs ) . The proposed scheme consists of a prostate segmentation stage and a tumor detection stage . In the first stage , we adopt a state-of-art fully convolutional network ( FCN ) architecture with residual connections to segment prostate areas from mpMRIs including T2-weighted ( T2W ) , T1 and diffusion-weighted imaging ( DWI ) .
Results are presented for situations when intensity only registration performs best , when intensity plus equally weighted surface registration performs best , and when intensity plus uncertainty weighted surface registration performs best . The algorithm has been applied to register CBCT and FBCT prostate images where the uncertainty of the prostate surface segmentation was estimated using contours drawn by five experts .
Moreover , MRI volumes are often affected by artefacts and distortions due to field inhomogeneity . Prostate segmentation is nevertheless an important task having clinical relevance both during diagnosis , where the volume of the prostate needs to be assessed , and during treatment planning , where the estimate of the anatomical boundary needs to be accurate . CNNs have been recently used for medical image segmentation .
Automatic segmentation of organs is a crucial task for computer-aided medical image analysis . Due to the specificity of location and morphology of the pancreas , it is challenging to segment pancreas from other surrounding abdominal organs .
This includes four atlas-based approaches , specifically median of atlas images ( A-Median ) [ 1 ] , atlas-based voxel-wise weighting ( A-VW ) [ 2 ] , bone enhanced atlas-based voxel-wise weighting ( A-Bone ) [ 3 ] , iterative atlas-based voxel-wise weighting ( A-Iter ) [ 4 ] , and a method based on deep learning convolutional neural network ( DL-CNN ) [ 5 ] . Automatic organ delineation was performed for bladder , rectum and bone . Overall , A-VW , A-Bone , A-Iter and A-VWexhibited comparable performance while DL-CNN showed slightly better segmentation performance resulting in Dice metrics of 0.93 , 0.90 , and 0.93 , respectively .
Some of the current interests are the automatic diagnosis of liver , spleen , and kidney pathologies and the 3D volume rendering of the abdominal organs . The first and fundamental step in all these studies is the automatic organs segmentation , that is still an open problem . In this paper we propose our fully automatic system that employs a hierarchical gray level based framework to segment heart , bones ( i.e. ribs and spine ) , liver and its blood vessels , kidneys , and spleen .
Its treatment can be helped by automatic detection of structures of interest in CT-scans . The automatic delimitation of organs close to the liver , of the liver itself , and of its vessels and tumors is a complex task . The aim of this work is to explore the tools of mathematical morphology , and to study how these tools can be used to improve the segmentation .
In particular , our work focuses on the segmentation of mitochondria in Serial Section Electron Microscopy images of the brain . Segmentation is a highly relevant task in medical image analysis , as automatic delineation of organs and structures of interest is often necessary to perform computer assisted diagnosis . All the experiments performed make use of the public Electron Microscopy Dataset , a representation of a section taken from the CA1 hippocampus region of the brain .
The automatic delineation of the boundaries of organs and other anatomical structures is a key component of many medical image processing systems . In this paper we present a generic learning approach based on a novel space of segmentation features , which can be trained to predict the overlap error and Dice coefficient of an arbitrary organ segmentation without knowing the ground truth delineation .
The atlas-based techniques can be problematic for complicated structures with anatomical variability . Deformable models are among the most popular methods for automatic detection of different organs in MR images . However they still have an important limitation which is that they are sensitive to initial position and shape of the model .
This paper describes the approach for automatic identifying organs from a medical CT imagery . Main assumption of this approach is the use of data sets and domain knowledge .
3 ) We use a novel approach to track the growing region changes across the CT image sequence in detecting region of interest , given a seed point as our input . We used quantitative and qualitative analysis to measure the accuracy against the given ground truth and our results presented a better performance than other generic approaches for automatic region of interest detection of organs in abdominal CT images . With the results presented in this research work , our proposed novel sequence approach method has been proven to be superior in terms of accuracy , automation and robustness .
Analysis of Wireless Capsule Endoscopy ( CE ) images has become a very active area of research since this novel technology enabled access to previously inaccessible areas of the gastrointestinal tract , especially the small intestine . Art has investigated automatic segmentation of organ boundaries , detection of lesions and bleeding as well as other supervised and unsupervised analysis . However , all of this art has focused on treating the images as individual and independent observations that contribute towards a unique and separate decision .
Segmentation is a highly relevant task in medical image analysis . Automatic delineation of organs and structures of interest is often necessary to perform tasks such as visual augmentation , computer assisted diagnosis , interventions and extraction of quantitative indices from images . In particular , since diagnostic and interventional imagery often consists of 3D images , being able to perform volumetric segmentations by taking into account the whole volume content at once , has a particular relevance .
: Recently , there has been an increasing trend to propose computer aided diagnosis systems for biomedical pattern recognition . A computer aided diagnosis method , which aims higher classification accuracy , is developed to classify the biomedical dataset .
Convolutional neural networks ( CNNs ) trained on natural images are extremely successful in image classification and localization due to superior automated feature extraction capability . In extending their use to biomedical recognition tasks , it is important to note that visual features of medical images tend to be uniquely different than natural images . There are advantages offered through training these networks on large scale medical common modality image collections pertaining to the recognition task .
In this paper , we present a biomedical name recognition system , called PowerBioNE . In order to deal with the special phenomena in the biomedical domain , various evidential features are proposed and integrated through a mutual information independence model ( MIIM ) .
Digital image processing and recognition were strongly developed on powerful workstations , however these procedures can also be implemented in PCs with DSP microprocessor cards or in situations where digital transforms used for image processing can be simply implemented and do not consume a lot of time . The example of biomedical image recognition performed in an optical way , by using a diffractometer , and in a digital system with a CCD camera will be described here .
In order to solve the problem in the image process researches , it is very important to find an efficient , biologic recognition related , and high-level semantic image classification method . By adopting the theories of biologic visual recognition and knowledge engineering combined with biologic characteristic(information , ) and using minimum spanning tree as an image information construction tree , this paper pre-(sents ) a sparse coding-based image semantic classifier ( SCISC ) model . The experimental result shows that this model has high accuracy in image classification .
There is a significant increase in the use of biomedical images in clinical medicine , disease research , and education . While the literature lists several successful methods that were developed and implemented for content-based image retrieval and recognition , they have been unable to make significant inroads in biomedical image recognition domain . The use of computer-aided diagnosis has been increasing .
We apply the wavelet-based image enhancement technique developed in our earlier work [ Y. Wang et al. , Proc . ICASSP 2001 , Salt Lake City , May , 2001 ] , to a well-known biomedical pattern recognition problem : chromosome classification . Experiments were conducted on a test set of chromosome images before and after the enhancement , using the same feature measurement and classifier methods .
Fractals are geometric objects that have a noninteger fractal dimension ( FD ) . The FD has been exploited for various biomedical recognition applications such as breast tumor and lung tumor detection . Our previous work shows that the FD is useful in the detection of brain tumors when a reference nontumor image is available .
The issues of features informativity evaluation in biomedical signals portraits according to formal comparison of their probability values , obtained during training of supervised learning recognition systems are being considered . The purpose of this work is to increase the effectiveness of the biomedical signals recognition in diagnostic systems with supervised learning , by choosing rational structure of portraits based on the nature of their elements influence on the quality of pattern recognition . Details .
As with many others types of cancer , early detection and treatment greatly increases the patient 's chance of survival . MRI prostate segmentation allows clinical personnel to design an accurate treatment plan . A novel method for MRI prostate imagery segmentation is proposed in this paper .
Computer-aided diagnosis and decision-making systems have shown great effectiveness in assisting doctors for this purpose . At the same time , deep learning based on Generative Adversarial Networks can be applied to the segmentation of prostate MR images . In this paper , we propose a new computer-aided diagnosis and decision-making system based on a deep learning model to automatically segment the prostate region from prostate MR images .
On MICCAI PROMISE12 test data sets , the proposed method yielded a mean Dice score of 84 % with a standard deviation of 4 % . The experimental results demonstrated that the proposed method outperformed other ASM-based prostate MRI segmentation methods and achieved a level of accuracy comparable to that of state-of-the-art methods .
Efficient computer-assisted segmentation approaches are not yet able to address these issues , but rather have the potential to do so . In this paper , a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means ( FCM ) clustering algorithm , which enables multispectral T1-weighted ( T1w ) and T2-weighted ( T2w ) MRI anatomical data processing , is proposed . This approach , using an unsupervised Machine Learning technique , helps to segment the prostate gland effectively .
In 12 cases , including prostates with cancer and benign prostatic hypertrophy , the mean Hausdorff distance and overlap ratio were 9.94 mm and 0.83 , respectively . ConclusionThis new automatic prostate MRI segmentation method produces satisfactory results , even at prostate ’s base and apex . The method is computationally feasible and efficient .
As a result , this kind of approaches faces many difficulties when segmenting the base and apex of the prostate due to the limited slice boundary information . To tackle this problem , in this paper , we propose a deep neural network with bidirectional convolutional recurrent layers for MRI prostate image segmentation . In addition to utilizing the intraslice contexts and features , the proposed model also treats prostate slices as a data sequence and utilizes the interslice contexts to assist segmentation .
However , success with prostate cancer localization based on current imaging methods has been limited due to overlap in feature space of benign and malignant tissues using any one MRI method and the interobserver variability . In this paper , we present a new unsupervised segmentation method for prostate cancer detection , using fuzzy Markov random fields ( fuzzy MRFs ) for the segmentation of multispectral MR prostate images . Typically , both hard and fuzzy MRF models have two groups of parameters to be estimated : the MRF parameters and class parameters for each pixel in the image .
( Segmentation of Prostate MRI using Probabilistic Atlas and Deformable Model ) . Le principal objectif de cette these est le developpement d'outils permettant le contourage automatique de la prostate en IRM .
NVFP is incorporated into a model based method for image segmentation . The performance of our proposed method was demonstrated by segmenting prostate MR images . The segmentation results indicated that our method can achieve better performance compared with other existing methods .
section : Conclusion We presented and approach based on a volumetric convolutional neural network that performs segmentation of MRI prostate volumes in a fast and accurate manner . We introduced a novel objective function that we optimise during training based on the Dice overlap coefficient between the predicted segmentation and the ground truth annotation .
Usefulness of fine-tuning for deep learning based multi-organ regions segmentation method from non-contrast CT volumes using small training dataset Medical image information marking method and device and medical device Pixel-based Machine Learning in Computer-Aided Diagnosis of Lung and Colon Cancer Robust algorithms for anatomic plane primitive detection in MR SKIN SEGMENTATION BASED ON IMPROVED THRESHOLDING METHOD Cognitive informatics in image semantics description, identification and automatic pattern understanding Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization Brain tumor segmentation with Deep Neural Networks V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Distance metric-based time-efficient fuzzy algorithm for abnormal magnetic resonance brain image segmentation RIANet: Recurrent interleaved attention network for cardiac MRI segmentation 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 Automatic high resolution segmentation of the prostate from multi-planar MRI Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 Tesla MRI A fast model-based prostate boundary segmentation using normalized cross-correlation and representative patterns in ultrasound images A supervoxel‐based segmentation method for prostate MR images Opportunities for image analysis in radiation oncology Automatic Magnetic Resonance Image Prostate Segmentation Based on Adaptive Feature Learning Probability Boosting Tree Initialization and CNN-ASM Refinement Automatic computer aided detection of abnormalities in multi-parametric prostate MRI Fully convolutional neural networks for prostate cancer detection using multi-parametric magnetic resonance images: an initial investigation SUPIR: Surface Uncertainty-Penalized, Non-rigid Image Registration for Pelvic CT Imaging V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation DualSLIC: An Automatic Coarse-to-Fine Method on Pancreas Segmentation Comparison of synthetic CT generation algorithms for MRI-only radiation planning in the pelvic region Automatic Abdominal Organ Segmentation from CT images Connected morphological operators for CT-scan images of the abdomen Segmentation of mitochondria in Serial Section Electron Microscopy images of the brain using Deep Neural Networks Evaluating Segmentation Error without Ground Truth Automatic Mesh-Based Segmentation of Multiple Organs in MR Images Automatic Medical Objects Classification Based on Data Sets and Domain Knowledge Novel Approach for Automatic Region of Interest and Seed Point Detection in CT Images based on Temporal and Spatial Data Towards integrating temporal information in capsule endoscopy image analysis V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Feature Selection using FFS and PCA in Biomedical Data Classification with AdaBoost-SVM Detection and visualization of abnormality in chest radiographs using modality-specific convolutional neural network ensembles Recognizing names in biomedical texts using mutual information independence model and SVM plus sigmoid Image processing and recognition using diffractive and digital techniques Sparse Coding-Based Image Semantic Classifier Model Intelligent Information Description and Recognition in Biomedical Image Databases The effect of image enhancement on biomedical pattern recognition Statistical analysis of fractal-based brain tumor detection algorithms. Критерії та процедури оцінки інформативності та відбору ознак медико-біологічних сигналів для їх розпізнавання Semi automatic MRI prostate segmentation based on wavelet multiscale products Computer-aided diagnosis and decision-making system for medical data analysis: A case study on prostate MR images Automatic Magnetic Resonance Image Prostate Segmentation Based on Adaptive Feature Learning Probability Boosting Tree Initialization and CNN-ASM Refinement Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect Prostate Cancer Segmentation With Simultaneous Estimation of Markov Random Field Parameters and Class Utilisation d'atlas anatomiques et de modèles déformables pour la segmentation de la prostate en IRM. (Segmentation of Prostate MRI using Probabilistic Atlas and Deformable Model) Medical Image Segmentation Using Descriptive Image Features V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
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Machine Learning in Healthcare Informatics Medical Imaging: Computer-Aided Diagnosis 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Medical Image Anal. 2016 Fourth International Conference on 3D Vision (3DV) Medical Image Anal. ArXiv IEEE Transactions on Pattern Analysis and Machine Intelligence ICACCI ISBI Medical Imaging: Computer-Aided Diagnosis Medical Imaging: Computer-Aided Diagnosis ICPR WBIR 2016 Fourth International Conference on 3D Vision (3DV) SMC MICCAI (1) BDAS EMBC 2016 Fourth International Conference on 3D Vision (3DV) BMVC 2016 Fourth International Conference on 3D Vision (3DV)
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