Accurate segmentation of critical anatomical structures is at the core of medical image analysis. The main bottleneck lies in gathering the requisite expert-labeled image annotations in a scalable manner. Methods that permit to produce accurate anatomical structure segmentation without using a large amount of fully annotated training images are highly desirable. In this work, we propose a novel contribution of Contour Transformer Network (CTN), a one-shot anatomy segmentor including a naturally built-in human-in-the-loop mechanism. Segmentation is formulated by learning a contour evolution behavior process based on graph convolutional networks (GCNs). Training of our CTN model requires only one labeled image exemplar and leverages additional unlabeled data through newly introduced loss functions that measure the global shape and appearance consistency of contours. We demonstrate that our one-shot learning method significantly outperforms non-learning-based methods and performs competitively to the state-of-the-art fully supervised deep learning approaches. With minimal human-in-the-loop editing feedback, the segmentation performance can be further improved and tailored towards the observer desired outcomes. This can facilitate the clinician designed imaging-based biomarker assessments (to support personalized quantitative clinical diagnosis) and outperforms fully supervised baselines.
Segmentation of medical images is a challenging task owing to their complexity. A standard segmentation problem within Magnetic Resonance Imaging (MRI) is the task of labeling voxels according to their tissue type. Image segmentation provides volumetric quantification of liver area and thus helps in the diagnosis of disorders, such as Hepatitis, Cirrhosis, Jaundice, Hemochromatosis etc.This work deals with comparison of segmentation by applying Level Set Method,Fuzzy Level Information C-Means Clustering Algorithm and Gradient Vector Flow Snake Algorithm.The results are compared using the param…
University of South Carolina（南卡罗来纳大学）；PAII Inc.（PAII公司）；University of Rochester（罗切斯特大学）；Chang Gung Memorial Hospital（长庚纪念医院）；Ping An Technology（平安科技）
Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gathering the requisite expert-labeled image annotations in a scalable manner remains a main obstacle. Therefore, annotation-efficient methods that permit to produce accurate anatomical structure segmentation are highly desirable. In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism. We formulate anatomy segmentation as a co…
Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervision
Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain（生物医学影像技术，电信工程学院，马德里理工大学，马德里，西班牙）；IHU PRISM National Precision Medicine Center in Oncology, Cancer Data Science Unit, CentraleSupélec, Gustave Roussy, INSERM, Université Paris-Saclay, Gif-sur-Yvette, France（IHU PRISM国家精准医学中心（肿瘤学），癌症数据科学单元，中央圣艾尔布兰，格斯塔夫·罗西，国家医学研究所，巴黎-萨克雷大学，法国）
The University of Hong Kong（香港大学）；Tencent（腾讯）；Huazhong University of Science and Technology（华中科技大学）
Learning by imitation is one of the most significant abilities of human beings and plays a vital role in human's computational neural system. In medical image analysis, given several exemplars (anchors), experienced radiologist has the ability to delineate unfamiliar organs by imitating the reasoning process learned from existing types of organs. Inspired by this observation, we propose OrganNet which learns a generalized organ concept from a set of annotated organ classes and then transfer this concept to unseen classes. In this paper, we show that such process can be integrated into the one-…
University of Sherbrooke（舍布鲁克大学）；Université de Bourgogne Franche-Comté（勃艮第-弗朗什孔泰大学）；CArdiac Simulation & Imaging Software（心脏模拟与成像软件公司）；Université de Lyon（里昂大学）
Recent publications have shown that the segmentation accuracy of modern-day convolutional neural networks (CNN) applied on cardiac MRI can reach the inter-expert variability, a great achievement in this area of research. However, despite these successes, CNNs still produce anatomically inaccurate segmentations as they provide no guarantee on the anatomical plausibility of their outcome, even when using a shape prior. In this paper, we propose a cardiac MRI segmentation method which always produces anatomically plausible results. At the core of the method is an adversarial variational autoencod…
University of Waterloo（滑铁卢大学）；Suez Canal University（苏伊士运河大学）
Quantifying the accuracy of segmentation and manual delineation of organs, tissue types and tumors in medical images is a necessary measurement that suffers from multiple problems. One major shortcoming of all accuracy measures is that they neglect the anatomical significance or relevance of different zones within a given segment. Hence, existing accuracy metrics measure the overlap of a given segment with a ground-truth without any anatomical discrimination inside the segment. For instance, if we understand the rectal wall or urethral sphincter as anatomical zones, then current accuracy measu…
Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues
HIDSS4Health - Helmholtz Information and Data Science School for Health（亥姆霍兹信息与健康数据科学学院（HIDSS4Health））；Institute for AI in Medicine (IKIM), University Hospital Essen（埃森大学医院医学人工智能研究所（IKIM））
Institute for Signals, Systems and Computational Intelligence, sinc(i) CONICET-UNL（信号、系统与计算智能研究所，sinc(i) 阿根廷国家科学技术研究委员会-国立利托拉尔大学）；Hospital Italiano de Buenos Aires（布宜诺斯艾利斯意大利医院）；Universidad Tecnológica Nacional（国立技术大学）
Anatomical segmentation is a fundamental task in medical image computing, generally tackled with fully convolutional neural networks which produce dense segmentation masks. These models are often trained with loss functions such as cross-entropy or Dice, which assume pixels to be independent of each other, thus ignoring topological errors and anatomical inconsistencies. We address this limitation by moving from pixel-level to graph representations, which allow to naturally incorporate anatomical constraints by construction. To this end, we introduce HybridGNet, an encoder-decoder neural archit…
Encoding Structural Constraints into Segment Anything Models via Probabilistic Graphical Models
The George Washington University（乔治·华盛顿大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Hospital（约翰斯·霍普金斯医院）
Visualizing disease-induced scarring and fibrosis in the heart on cardiac magnetic resonance (CMR) imaging with contrast enhancement (LGE) is paramount in characterizing disease progression and quantifying pathophysiological substrates of arrhythmias. However, segmentation and scar/fibrosis identification from LGE-CMR is an intensive manual process prone to large inter-observer variability. Here, we present a novel fully-automated anatomically-informed deep learning solution for left ventricle (LV) and scar/fibrosis segmentation and clinical feature extraction from LGE-CMR. The technology invo…
University of Electronic Science and Technology of China（电子科技大学）；Shanghai AI Lab（上海人工智能实验室）；SenseTime Research（商汤科技研究院）；Shanghai Jiao Tong University（上海交通大学）；ShanghaiTech University（上海科技大学）；West China Hospital, Sichuan University（四川大学华西医院）
Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for medical image segmentation, yet need plenty of manual annotations for training. Semi-Supervised Learning (SSL) methods are promising to reduce the requirement of annotations, but their performance is still limited when the dataset size and the number of annotated images are small. Leveraging existing annotated datasets with similar anatomical structures to assist training has a potential for improving the model's performance. However, it is further challenged by the cross-anatomy domain shift due to the differe…
Optical coherence tomography (OCT) is a non-invasive 3D modality widely used in ophthalmology for imaging the retina. Achieving automated, anatomically coherent retinal layer segmentation on OCT is important for the detection and monitoring of different retinal diseases, like Age-related Macular Disease (AMD) or Diabetic Retinopathy. However, the majority of state-of-the-art layer segmentation methods are based on purely supervised deep-learning, requiring a large amount of pixel-level annotated data that is expensive and hard to obtain. With this in mind, we introduce a semi-supervised paradi…
Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper we investigate the latest fully-convolutional architectures for the task of multi-class segmentation of the lungs field, heart and clavicles in a chest radiograph. In addition, we explore the influence of using different loss functions in the training process of a neural network for semantic segmentation. We evaluate all models on a common benchmark of 247 X-ray images from the JSRT database and ground-truth segmentation masks from the SCR dataset. Our best pe…
SD-RetinaNet: Topologically Constrained Semi-Supervised Retinal Lesion and Layer Segmentation in OCT
Christian Doppler Laboratory for Artificial Intelligence in Retina（基督教多普勒人工智能视网膜实验室）；Institute of Artificial Intelligence（人工智能研究所）；Center for Medical Data Science（医学数据科学中心）；Medical University of Vienna（维也纳医学大学）；Computational Imaging Research Lab（计算成像研究实验室）；Department of Biomedical Imaging and Image-Guided Therapy（生物医学成像与影像引导治疗部门）；Department of Ophthalmology and Optometry（眼科与视光学部门）
University of Sherbrooke（舍布鲁克大学）；University of Bourgogne Franche-Comte（勃艮第-弗朗什孔泰大学）；CASIS inc.（CASIS公司）；University of Lyon（里昂大学）
Convolutional neural networks (CNN) have had unprecedented success in medical imaging and, in particular, in medical image segmentation. However, despite the fact that segmentation results are closer than ever to the inter-expert variability, CNNs are not immune to producing anatomically inaccurate segmentations, even when built upon a shape prior. In this paper, we present a framework for producing cardiac image segmentation maps that are guaranteed to respect pre-defined anatomical criteria, while remaining within the inter-expert variability. The idea behind our method is to use a well-trai…
AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning
Department of Radiology, Northwestern University, Chicago, IL, USA（西北大学放射科）；Data Science Institute, Vanderbilt University, Nashville, TN, USA（范德比尔特大学数据科学研究院）；Department of Orthopedics, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, P.R. China（山西医科大学第二医院骨科）；Second Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, P.R. China（山西医科大学第二临床医学院）；School of Computer Science, University of Sydney, Sydney, NSW, Australia（悉尼大学计算机科学学院）
SegVol: Universal and Interactive Volumetric Medical Image Segmentation
School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；BAAI（北京智源人工智能研究院）；The Chinese University of Hong Kong（香港中文大学）；Peking University（北京大学）
Improving Clinical Target Volume Segmentation Accuracy using Anatomical Priors and Active Learning for the AGITG TOPGEAR Clinical Trial
University of New South Wales（新南威尔士大学）；Ingham Institute for Applied Medical Research（英厄姆应用医学研究所）；Liverpool and Macarthur Cancer Therapy Centres（利物浦与麦克阿瑟癌症治疗中心）；Peter MacCallum Cancer Centre（彼得·麦卡勒姆癌症中心）；University of Melbourne（墨尔本大学）；CSIRO Australian e-Health Research Centre（联邦科学与工业研究组织澳大利亚电子健康研究中心）；University of Sydney（悉尼大学）；The University of Western Australia（西澳大学）；Sir Charles Gardiner Hospital（查尔斯·加德纳爵士医院）；University of Wisconsin（威斯康星大学）；Radformation Inc.（Radformation公司）
Université Côte d’Azur（蔚蓝海岸大学）；Inria（法国国家信息与自动化研究所）；Centre Antoine Lacassagne（安托万·拉卡桑中心）
Planning of radiotherapy involves accurate segmentation of a large number of organs at risk, i.e. organs for which irradiation doses should be minimized to avoid important side effects of the therapy. We propose a deep learning method for segmentation of organs at risk inside the brain region, from Magnetic Resonance (MR) images. Our system performs segmentation of eight structures: eye, lens, optic nerve, optic chiasm, pituitary gland, hippocampus, brainstem and brain. We propose an efficient algorithm to train neural networks for an end-to-end segmentation of multiple and non-exclusive class…
Bruch's membrane (BM) segmentation on optical coherence tomography (OCT) is a pivotal step for the diagnosis and follow-up of age-related macular degeneration (AMD), one of the leading causes of blindness in the developed world. Automated BM segmentation methods exist, but they usually do not account for the anatomical coherence of the results, neither provide feedback on the confidence of the prediction. These factors limit the applicability of these systems in real-world scenarios. With this in mind, we propose an end-to-end deep learning method for automated BM segmentation in AMD patients.…
Effective, robust, and automatic tools for brain tumor segmentation are needed for the extraction of information useful in treatment planning from magnetic resonance (MR) images. Context-aware artificial intelligence is an emerging concept for the development of deep learning applications for computer-aided medical image analysis. In this work, it is investigated whether the addition of contextual information from the brain anatomy in the form of white matter, gray matter, and cerebrospinal fluid masks and probability maps improves U-Net-based brain tumor segmentation. The BraTS2020 dataset wa…
University of Science and Technology of China（中国科学技术大学）
Segmentation of multiple anatomical structures is of great importance in medical image analysis. In this study, we proposed a $\mathcal{W}$-net to simultaneously segment both the optic disc (OD) and the exudates in retinal images based on the multi-task learning (MTL) scheme. We introduced a class-balanced loss and a multi-task weighted loss to alleviate the imbalanced problem and to improve the robustness and generalization property of the $\mathcal{W}$-net. We demonstrated the effectiveness of our approach by applying five-fold cross-validation experiments on two public datasets e\_ophtha\_E…
Wellman Center for Photomedicine, Massachusetts General Hospital（马萨诸塞州总医院韦尔曼光医学中心）；Harvard Medical School（哈佛医学院）；Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；Erasmus Medical Center（伊拉斯谟医学中心）
Intravascular ultrasound and optical coherence tomography are widely available for characterizing coronary stenoses and provide critical vessel parameters to optimize percutaneous intervention. Intravascular polarization-sensitive optical coherence tomography (PS-OCT) simultaneously provides high-resolution cross-sectional images of vascular structures while also revealing preponderant tissue components such as collagen and smooth muscle and thereby enhances plaque characterization. Automated interpretation of these features promises to facilitate the objective clinical investigation of the na…
Carotid artery vessel wall thickness measurement is an essential step in the monitoring of patients with atherosclerosis. This requires accurate segmentation of the vessel wall, i.e., the region between an artery's lumen and outer wall, in black-blood magnetic resonance (MR) images. Commonly used convolutional neural networks (CNNs) for semantic segmentation are suboptimal for this task as their use does not guarantee a contiguous ring-shaped segmentation. Instead, in this work, we cast vessel wall segmentation as a multi-task regression problem in a polar coordinate system. For each carotid a…
AG-CRC: Anatomy-Guided Colorectal Cancer Segmentation in CT with Imperfect Anatomical Knowledge
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Digital Medicine Innovation Center（上海数字医学创新中心）；Ruijin Hospital（瑞金医院）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）
CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images
Institute for Signals, Systems and Computational Intelligence, sinc(i) CONICET-UNL（信号、系统与计算智能研究所（sinc(i) CONICET-UNL））；Hospital Italiano de Buenos Aires（布宜诺斯艾利斯意大利医院）；Universidad Tecnológica Nacional（国立技术大学）
From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation
Gran Sasso Science Institute (GSSI)（大萨索科学研究所）；European Hospital（欧洲医院）；University of Edinburgh（爱丁堡大学）；Physics of Fluids Group, University of Twente（特温特大学流体物理小组）；INFN–Laboratori Nazionali del Gran Sasso（意大利国家核物理研究所大萨索国家实验室）
Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（马萨诸塞州总医院与哈佛医学院戈登医学影像中心）
Lesions or organ boundaries visible through medical imaging data are often ambiguous, thus resulting in significant variations in multi-reader delineations, i.e., the source of aleatoric uncertainty. In particular, quantifying the inter-observer variability of manual annotations with Magnetic Resonance (MR) Imaging data plays a crucial role in establishing a reference standard for various diagnosis and treatment tasks. Most segmentation methods, however, simply model a mapping from an image to its single segmentation map and do not take the disagreement of annotators into consideration. In ord…
Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；Intel Corporation（英特尔公司）；University of Copenhagen（哥本哈根大学）
Adversarial attacks consist in maliciously changing the input data to mislead the predictions of automated decision systems and are potentially a serious threat for automated medical image analysis. Previous studies have shown that it is possible to adversarially manipulate automated segmentations produced by neural networks in a targeted manner in the white-box attack setting. In this article, we studied the effectiveness of adversarial attacks in targeted modification of segmentations of anatomical structures in chest X-rays. Firstly, we experimented with using anatomically implausible shape…
Muscle volume quantification: guiding transformers with anatomical priors
Nantes Université（南特大学）；École Centrale Nantes（南特中央理工学院）；Technical University of Munich (TUM)（慕尼黑工业大学）；INSEP（法国国家体育学院）；Fédération Française de Badminton（法国羽毛球联合会）
CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography
Department of Quantitative Biomedicine, University of Zurich（定量生物医学系，苏黎世大学）；ETH AI Center, ETH Zurich（ETH人工智能中心，苏黎世联邦理工学院）；Department of Radiation Oncology, University Hospital and University of Zurich（放射肿瘤科，苏黎世大学医院和苏黎世大学）；Faculty of Medicine, University of Zurich（医学系，苏黎世大学）；Department of Neurology and Clinical Neuroscience Center, University Hospital Zurich and University of Zurich（神经科和临床神经科学中心，苏黎世大学医院和苏黎世大学）；International School of Medicine, Istanbul Medipol University（国际医学院，伊斯坦布尔Medipol大学）；School of Computation, Information and Technology, Technical University of Munich（计算、信息与技术学院，慕尼黑技术大学）；Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, Technical University of Munich（诊断与介入神经放射科，医学与健康学院，慕尼黑技术大学）；Department of Radiation Oncology, TUM University Hospital Rechts der Isar, TUM School of Medicine and Health, Technical University of Munich（放射肿瘤科，慕尼黑技术大学医院Rechts der Isar，慕尼黑技术大学医学与健康学院）；Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU)（放射医学研究所（IRM），亥姆霍兹慕尼黑研究中心（HMGU））；German Consortium for Translational Cancer Research (DKTK), Partner Site Munich, Munich, Germany（德国转化癌症研究联盟（DKTK），慕尼黑分部，德国）；University Hospital Essen, Institute of Interventional and Diagnostic Radiology and Neuroradiology（埃森大学医院，介入与诊断放射学及神经放射学研究所）；University Hospital Essen, Institute for Artificial Intelligence in Medicine（埃森大学医院，医学人工智能研究所）；Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock（诊断与介入放射学、儿童放射学及神经放射学研究所，罗斯托克大学医学中心）；Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna（生物医学成像与图像引导治疗系，维也纳医科大学）；Department of Radiology, University Hospital Basel（放射科，巴塞尔大学医院）；Department of Radiology, Cambridge University Hospitals NHS Foundation Trust（放射科，剑桥大学医院 NHS 基础信托）；Labelata GmbH（Labelata 公司）；Visual and Data-Centric Computing, Zuse Institute Berlin (ZIB)（视觉与数据导向计算，柏林 Zuse 研究所（ZIB））；Brigham and Women’s Hospital（布里格姆和女性医院）
SRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Biomedical Perception & Intelligence Lab, University of South Dakota（南达科他大学生物医学感知与智能实验室）；Aalto University（阿尔托大学）；Malaviya National Institute of Technology Jaipur（马拉维亚国家理工学院斋浦尔分校）；Machine & Hybrid Intelligence Lab, Northwestern University（西北大学机器与混合智能实验室）
Shanghai Jiaotong University（上海交通大学）；Shanghai AI Lab（上海人工智能实验室）；University of Electronic Science and Technology of China（电子科技大学）；Sensetime Research（商汤科技研究院）
Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation. One-shot segmentation and weakly-supervised learning are promising research directions that lower labeling effort by learning a new class from only one annotated image and utilizing coarse labels instead, respectively. Previous works usually fail to leverage the anatomical structure and suffer from class imbalance and low contrast problems. Hence, we present an innovative framework for 3D medical image segmentation with one-shot and weakly-supervised…
Imperial College London（伦敦帝国理工学院）；National Heart and Lung Institute（国家心肺研究所）；MRC London Institute of Medical Sciences（医学研究委员会伦敦医学科学研究所）
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks…
CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration
University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心）；jung diagnostics（jung诊断公司）；University of Cambridge（剑桥大学）；Radboud University Medical Center（拉德堡德大学医学中心）；Early Cancer Institute（早期癌症研究所）；Cancer Research UK Cambridge Centre（英国癌症研究剑桥中心）
MOIS-SAM2: Exemplar-based Segment Anything Model 2 for multilesion interactive segmentation of neurofibromas in whole-body MRI
Institute for Applied Medical Informatics, Institute of Computational Neuroscience, and Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf（应用医学信息学研究所、计算神经科学研究所和生物医学人工智能中心（bAIome）、汉堡医学院中心）；Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf（诊断与介入放射学及核医学系、汉堡医学院中心）；Department of Neurology, University Medical Center Hamburg-Eppendorf（神经病学系、汉堡医学院中心）
MOIS-SAM2是一种基于示例的Segment Anything Model 2，用于全身体部MRI中神经纤维瘤多病灶交互分割，实现了高精度和可扩展性，支持临床应用。
Seoul National University（首尔国立大学）；Adobe Research（奥多比研究院）
We extend panoptic segmentation to the open-world and introduce an open-set panoptic segmentation (OPS) task. This task requires performing panoptic segmentation for not only known classes but also unknown ones that have not been acknowledged during training. We investigate the practical challenges of the task and construct a benchmark on top of an existing dataset, COCO. In addition, we propose a novel exemplar-based open-set panoptic segmentation network (EOPSN) inspired by exemplar theory. Our approach identifies a new class based on exemplars, which are identified by clustering and employe…
We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data that contains images and their manual segmentations. Instead, we employ unpaired segmentation images to build an anatomical prior. Critically these segmentations can be derived from imaging data from a different dataset and imaging modality than the current task. We introduce a generative probabilistic model that employs the learned prior through a convolutional neural network to compute segmentations in an unsupervise…
We propose a 2D Encoder-Decoder based deep learning architecture for semantic segmentation, that incorporates anatomical priors by imitating the encoder component of an autoencoder in latent space. The autoencoder is additionally enhanced by means of hierarchical features, extracted by an U-Net module. Our suggested architecture is trained in an end-to-end manner and is evaluated on the example of pelvic bone segmentation in MRI. A comparison to the standard U-Net architecture shows promising improvements.
University of California, Los Angeles（加州大学洛杉矶分校）；RedNova Health
Label propagation is a popular technique for anatomical segmentation. In this work, we propose a novel deep framework for label propagation based on non-local label fusion. Our framework, named CompareNet, incorporates subnets for both extracting discriminating features, and learning the similarity measure, which lead to accurate segmentation. We also introduce the voxel-wise classification as an unary potential to the label fusion function, for alleviating the search failure issue of the existing non-local fusion strategies. Moreover, CompareNet is end-to-end trainable, and all the parameters…
The Chinese University of Hong Kong（香港中文大学）；Siemens Healthcare（西门子医疗）
Accurate detection and segmentation of anatomical structures from ultrasound images are crucial for clinical diagnosis and biometric measurements. Although ultrasound imaging has been widely used with superiorities such as low cost and portability, the fuzzy border definition and existence of abounding artifacts pose great challenges for automatically detecting and segmenting the complex anatomical structures. In this paper, we propose a multi-domain regularized deep learning method to address this challenging problem. By leveraging the transfer learning from cross domains, the feature represe…
