Our contribution is a unified cross-modality feature disentagling approach for multi-domain image translation and multiple organ segmentation. Using CT as the labeled source domain, our approach learns to segment multi-modal (T1-weighted and T2-weighted) MRI having no labeled data. Our approach uses a variational auto-encoder (VAE) to disentangle the image content from style. The VAE constrains the style feature encoding to match a universal prior (Gaussian) that is assumed to span the styles of all the source and target modalities. The extracted image style is converted into a latent style scaling code, which modulates the generator to produce multi-modality images according to the target domain code from the image content features. Finally, we introduce a joint distribution matching discriminator that combines the translated images with task-relevant segmentation probability maps to further constrain and regularize image-to-image (I2I) translations. We performed extensive comparisons to multiple state-of-the-art I2I translation and segmentation methods. Our approach resulted in the lowest average multi-domain image reconstruction error of 1.34$\pm$0.04. Our approach produced an average Dice similarity coefficient (DSC) of 0.85 for T1w and 0.90 for T2w MRI for multi-organ segmentation, which was highly comparable to a fully supervised MRI multi-organ segmentation network (DSC of 0.86 for T1w and 0.90 for T2w MRI).
Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentation has been achieved by deep learning models, i.e., convolutional neural network (CNN). However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs. In this work, we tackle this problem by introducing a novel end-to-end shape learning architecture -- organ point-network. It takes deep learning features as inputs and generates organ shape represent…
AbdomenGen: Sequential Volume-Conditioned Diffusion Framework for Abdominal Anatomy Generation
Center for Virtual Imaging Trials, Dept. of Radiology, Duke University School of Medicine（虚拟成像试验中心，放射科，杜克大学医学部）；Dept. of Electrical and Computer Engineering, Pratt School of Engineering, Duke University（电气与计算机工程系，普拉特工程学院，杜克大学）；Dept. of Physics, Trinity School of Arts and Sciences, Duke University（物理系，特里尼蒂艺术与科学学院，杜克大学）
AbdomenGen提出了一种序列体积条件扩散框架，通过Volume Control Scalar实现可控的腹部解剖生成，实现了高几何保真度和多器官解耦调节。
MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI
Radiology and Imaging Sciences（放射学与影像科学）；National Institutes of Health Clinical Center（国家卫生研究院临床中心）；Walter Reed National Military Medical Center（沃尔特·里德国家军事医学中心）；Biostatistics and Clinical Epidemiology Services（生物统计与临床流行病学服务）
Accurate segmentation of multiple organs of the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation method has far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. For the first time, from the perspective of full and imperfect annotation, we comprehensively compile 1…
Mansoura University（曼苏尔大学）；South Valley University（南河谷大学）；Higher Institute of Engineering and Technology（高等工程技术学院）
The liver is one of the most critical metabolic organs in vertebrates due to its vital functions in the human body, such as detoxification of the blood from waste products and medications. Liver diseases due to liver tumors are one of the most common mortality reasons around the globe. Hence, detecting liver tumors in the early stages of tumor development is highly required as a critical part of medical treatment. Many imaging modalities can be used as aiding tools to detect liver tumors. Computed tomography (CT) is the most used imaging modality for soft tissue organs such as the liver. This …
Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation
National Engineering Research Center for Multimedia Software（多媒体软件国家工程研究中心）；Hubei Key Laboratory of Multimedia and Network Communication Engineering（湖北省多媒体与网络通信工程重点实验室）；Institute of Artificial Intelligence（人工智能研究院）；School of Computer Science（计算机科学学院）
Two-Stage Hybrid Supervision Framework for Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT
Beijing University of Posts and Telecommunications（北京邮电大学）；Dalian University of Technology（大连理工大学）；Guilin University of Electronic Technology（桂林电子科技大学）
Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge
University of Toronto（多伦多大学）；University Health Network（大学网络健康中心）；Vector Institute（向量研究所）；Lenovo Research（联想研究院）；Nanjing Anke Medical Technology Co., Ltd.（南京安科医疗科技有限公司）；Ocean University of China（中国海洋大学）；ShenZhen Yorktal DMIT Co. LTD.（深圳约克塔尔DMIT有限公司）；Shanghai Jiao Tong University（上海交通大学）；East China University of Science and Technology（华东理工大学）；Shanghai AI Laboratory（上海人工智能实验室）；City University of Macau（澳门城市大学）；Hanglok-Tech Co., Ltd.（恒乐科技有限公司）；Nanjing Drum Tower Hospital（南京鼓楼医院）
Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble
Zhejiang University（浙江大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；Ritsumeikan University（立命馆大学）；University of Science and Technology of China（中国科学技术大学）
ORACLE-CT: Anatomy-Aware Support Pooling for CT Classification
Center for Virtual Imaging Trials, RAI Labs, Department of Radiology, Duke University（虚拟成像试验中心，RAI实验室，放射学系，杜克大学）；Electrical and Computer Engineering, Pratt School of Engineering, Duke University（电气与计算机工程，工程学院，杜克大学）；Department of Mathematics, Trinity College of Arts & Sciences, Duke University（数学系，艺术与科学学院，杜克大学）；Department of Radiology and Imaging Sciences, University of Arizona College of Medicine（放射学与影像科学系，亚利桑那大学医学院）
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Artificial Intelligence Research Institute (AIRI)（人工智能研究所（AIRI））；Institute for Information Transmission Problems（信息传输问题研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）
Accurate and robust abdominal multi-organ segmentation from CT imaging of different modalities is a challenging task due to complex inter- and intra-organ shape and appearance variations among abdominal organs. In this paper, we propose a probabilistic multi-organ segmentation network with hierarchical spatial-wise feature modulation to capture flexible organ semantic variants and inject the learnt variants into different scales of feature maps for guiding segmentation. More specifically, we design an input decomposition module via a conditional variational auto-encoder to learn organ-specific…
University College London（伦敦大学学院）；InstaDeep；University of Westminster（威斯敏斯特大学）
Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment t…
University of Electronic Science and Technology of China（电子科技大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Sichuan Cancer Hospital and Institute（四川省肿瘤医院（四川省肿瘤研究所））；Sichuan Cancer Center（四川省癌症中心）；Sichuan University（四川大学）；West China Hospital（华西医院）；Johns Hopkins University（约翰斯·霍普金斯大学）；SenseTime Research（商汤科技研究院）；Rutgers University（罗格斯大学）
Whole abdominal organ segmentation is important in diagnosing abdomen lesions, radiotherapy, and follow-up. However, oncologists' delineating all abdominal organs from 3D volumes is time-consuming and very expensive. Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation. In this work, we establish a new large-s…
University of Central Florida（中佛罗里达大学）；National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究实验室）
Abdominal fat quantification is critical since multiple vital organs are located within this region. Although computed tomography (CT) is a highly sensitive modality to segment body fat, it involves ionizing radiations which makes magnetic resonance imaging (MRI) a preferable alternative for this purpose. Additionally, the superior soft tissue contrast in MRI could lead to more accurate results. Yet, it is highly labor intensive to segment fat in MRI scans. In this study, we propose an algorithm based on deep learning technique(s) to automatically quantify fat tissue from MR images through a c…
ReportMedSAM: Guiding Segmentation Through Radiology Reports
School of Engineering, College of Engineering and Physical Sciences, University of Birmingham（工程学院，工程与物理科学学院，伯明翰大学）；Department of Cancer and Genomic Sciences, College of Medicine and Health, University of Birmingham（癌症与基因组科学系，医学与健康学院，伯明翰大学）；Department of Computer Science, Faculty of Science and Engineering, University of Manchester（计算机科学系，科学与工程学院，曼彻斯特大学）；The Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine, and Health, University of Manchester（信息学、成像与数据科学部，生物、医学与健康学院，曼彻斯特大学）
Xiamen University（厦门大学）；Chinese PLA General Hospital（中国人民解放军总医院）；Deepwise AI Laboratory（深智云AI实验室）
Despite deep convolutional neural networks achieved impressive progress in medical image computing and analysis, its paradigm of supervised learning demands a large number of annotations for training to avoid overfitting and achieving promising results. In clinical practices, massive semantic annotations are difficult to acquire in some conditions where specialized biomedical expert knowledge is required, and it is also a common condition where only few annotated classes are available. In this work, we proposed a novel method for few-shot medical image segmentation, which enables a segmentatio…
The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography
Radboud University Medical Center（拉德堡德大学医学中心）；Jeroen Bosch Hospital（杰伦博世医院）；University Medical Center Groningen（格罗宁根大学医学中心）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学梅维斯研究所）
Oslo University Hospital（奥斯陆大学医院）；Mayo Clinic（梅奥诊所）；Nvidia Corporation（英伟达公司）
Image segmentation plays an essential role in medicine for both diagnostic and interventional tasks. Segmentation approaches are either manual, semi-automated or fully-automated. Manual segmentation offers full control over the quality of the results, but is tedious, time consuming and prone to operator bias. Fully automated methods require no human effort, but often deliver sub-optimal results without providing users with the means to make corrections. Semi-automated approaches keep users in control of the results by providing means for interaction, but the main challenge is to offer a good t…
xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；KOKONI, Moxin (Huzhou) Tech. Co., LTD.（墨芯（湖州）科技有限公司KOKONI）；School of Information Engineering, Huzhou University（湖州师范学院信息工程学院）；Information Systems Technology and Design Pillar, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计 pillar）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）
DeepVoxel Inc（深体素公司）；Shanghai General Hospital（上海市第一人民医院）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；University of California, Irvine（加州大学欧文分校）
Organs-at-risk (OAR) delineation in computed tomography (CT) is an important step in Radiation Therapy (RT) planning. Recently, deep learning based methods for OAR delineation have been proposed and applied in clinical practice for separate regions of the human body (head and neck, thorax, and abdomen). However, there are few researches regarding the end-to-end whole-body OARs delineation because the existing datasets are mostly partially or incompletely annotated for such task. In this paper, our proposed end-to-end convolutional neural network model, called \textbf{AttentionAnatomy}, can be …
Vanderbilt University（范德堡大学）；Sigma Technologies（12西格玛科技公司）；Vanderbilt University Medical Center（范德堡大学医学中心）
Segmentation of abdominal computed tomography(CT) provides spatial context, morphological properties, and a framework for tissue-specific radiomics to guide quantitative Radiological assessment. A 2015 MICCAI challenge spurred substantial innovation in multi-organ abdominal CT segmentation with both traditional and deep learning methods. Recent innovations in deep methods have driven performance toward levels for which clinical translation is appealing. However, continued cross-validation on open datasets presents the risk of indirect knowledge contamination and could result in circular reason…
TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration
Department of Imaging, Radboudumc, Nijmegen, the Netherlands（影像部，Radboudumc，尼姆egen，荷兰）；Institute for Medical Informatics, University of Lübeck, Lübeck, Germany（医学信息研究所，吕贝克大学，吕贝克，德国）；Department of Nuclear Medicine, Hospital 108, Hanoi, Vietnam（核医学部，108医院，河内，越南）；Department of Diagnostic Imaging and Interventional Radiology, Thai Nguyen National Hospital, Thai Nguyen, Vietnam（诊断影像和介入放射科，Thai Nguyen国家医院，Thai Nguyen，越南）；Interventional Radiology Center, Tam Anh Hospital, Hanoi, Vietnam（介入放射科中心，Tam Anh医院，河内，越南）；FET, Vietnam National University, University of Engineering and Technology, Hanoi, Vietnam（FET，越南国家大学，工程与技术大学，河内，越南）
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Siemens Healthineers（西门子医疗）；Annalise-AI, Pty, Ltd（Annalise人工智能私人有限公司）；Google Cloud AI（谷歌云人工智能）
Precise segmentation of the liver is critical for computer-aided diagnosis such as pre-evaluation of the liver for living donor-based transplantation surgery. This task is challenging due to the weak boundaries of organs, countless anatomical variations, and the complexity of the background. Computed tomography (CT) scanning and magnetic resonance imaging (MRI) images have different parameters and settings. Thus, images acquired from different modalities differ from one another making liver segmentation challenging task. We propose an efficient liver segmentation with the combination of holist…
