Body tissue composition is a long-known biomarker with high diagnostic and prognostic value in cardiovascular, oncological and orthopaedic diseases, but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging. Therefore an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known hounsfield unit limits. The Sørensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99. Our results show that fully-automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analysing body composition in the clinical routine.
Institute of Pathology, University Hospital Carl Gustav Carus (UKD), TU Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院病理研究所）；ASGEN GmbH & Co. KG（ASGEN两合有限责任公司）；National Center for Tumor Diseases (NCT), Partner Site Dresden（国家肿瘤疾病中心（德累斯顿合作站点））；ContextVision AB（康泰视伦公司）；Institute for Medical Informatics and Biometry (IMB), Carl Gustav Carus Faculty of Medicine, TU Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院医学信息学与生物测量学研究所）；Institute of Bioengineering, School of Life Sciences, EPFL（洛桑联邦理工学院生命科学学院生物工程研究所）；Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)（马克斯·普朗克分子细胞生物学与遗传学研究所）；Center for Systems Biology Dresden (CSBD)（德累斯顿系统生物学中心）
Histo-pathological diagnostics are an inherent part of the everyday work but are particularly laborious and associated with time-consuming manual analysis of image data. In order to cope with the increasing diagnostic case numbers due to the current growth and demographic change of the global population and the progress in personalized medicine, pathologists ask for assistance. Profiting from digital pathology and the use of artificial intelligence, individual solutions can be offered (e.g. detect labeled cancer tissue sections). The testing of the human epidermal growth factor receptor 2 (HER…
Simon Fraser University（西蒙弗雷泽大学）；Voronoi Health Analytics Incorporated（沃罗诺伊健康分析公司）
The latest advances in computer-assisted precision medicine are making it feasible to move from population-wide models that are useful to discover aggregate patterns that hold for group-based analysis to patient-specific models that can drive patient-specific decisions with regard to treatment choices, and predictions of outcomes of treatment. Body Composition is recognized as an important driver and risk factor for a wide variety of diseases, as well as a predictor of individual patient-specific clinical outcomes to treatment choices or surgical interventions. 3D CT images are routinely acqui…
Ege University（埃格大学）；National Institute on Aging, National Institutes of Health（美国国立卫生研究院国家老龄化研究所）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；University of Central Florida（中佛罗里达大学）
Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue composition analysis due to its excellent soft tissue contrast and lack of ionizing radiation. However, quantification of body composition requires an accurate segmentation of fat, muscle and other tissues from MR images, which remains a challenging goal due to the intensity overlap between them. In this study, we propose a fully automated, data-driven image segmentation platform that addresses multiple difficulties in segmenting MR images such as varying inhomogeneity, non-standardness, and noise, while pr…
Identification and quantification of nuclei in colorectal cancer haematoxylin \& eosin (H\&E) stained histology images is crucial to prognosis and patient management. In computational pathology these tasks are referred to as nuclear segmentation, classification and composition and are used to extract meaningful interpretable cytological and architectural features for downstream analysis. The CoNIC challenge poses the task of automated nuclei segmentation, classification and composition into six different types of nuclei from the largest publicly known nuclei dataset - Lizard. In this regard, w…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Oregon（俄勒冈大学）
The standard petrography test method for measuring air voids in concrete (ASTM C457) requires a meticulous and long examination of sample phase composition under a stereomicroscope. The high expertise and specialized equipment discourage this test for routine concrete quality control. Though the task can be alleviated with the aid of color-based image segmentation, additional surface color treatment is required. Recently, deep learning algorithms using convolutional neural networks (CNN) have achieved unprecedented segmentation performance on image testing benchmarks. In this study, we investi…
Cellular composition prediction, i.e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosin (H&E) stained tissue section can be used for various tasks in computational pathology such as the analysis of cellular topology and interactions, subtype prediction, survival analysis, etc. In this work, we propose an image-based cellular composition predictor (ALBRT) which can accurately predict the presence and counts of different types of cells in a given image patch. ALBRT, by its contrastive-learning inspired …
University of Pittsburgh School of Medicine（匹兹堡大学医学院）；VA Pittsburgh Healthcare System（匹兹堡退伍军人医疗系统）
Purpose: To develop and validate a computer tool for automatic and simultaneous segmentation of body composition depicted on computed tomography (CT) scans for the following tissues: visceral adipose (VAT), subcutaneous adipose (SAT), intermuscular adipose (IMAT), skeletal muscle (SM), and bone. Approach: A cohort of 100 CT scans acquired from The Cancer Imaging Archive (TCIA) was used - 50 whole-body positron emission tomography (PET)-CTs, 25 chest, and 25 abdominal. Five different body compositions were manually annotated (VAT, SAT, IMAT, SM, and bone). A training-while-annotating strategy w…
AI-Driven Automated Tool for Abdominal CT Body Composition Analysis in Gastrointestinal Cancer Management
Peking University（北京大学）；Peking University Cancer Hospital and Institute（北京大学肿瘤医院暨肿瘤研究所）；Beijing International Center for Mathematical Research (BICMR), Peking University（北京大学北京国际数学研究中心）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）
Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis
Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）；Department of Radiology, Duke University（杜克大学放射学系）；Department of Surgery Duke University School of Medicine（杜克大学医学学院外科系）；Department of Biostatistics & Bioinformatics, Duke University（杜克大学生物统计学与生物信息学系）；Department of Computer Science, Duke University（杜克大学计算机科学系）
The Second Hospital of Shandong University, Shandong University（山东大学第二医院，山东大学）；Shenzhen Third People’s Hospital, the Second Affiliated Hospital of Southern University of Science and Technology（深圳市第三人民医院，南方科技大学第二附属医院）；The First Affiliated Hospital of Hainan Medical University（海南医科大学第一附属医院）；Wuhan Asia General Hospital（武汉亚洲总医院）；Shanxi Medical University（山西医科大学）；Chengdu First People’s Hospital（成都市第一人民医院）；Keck School of Medicine, University of Southern California（南加州大学凯克医学院）；Shandong Provincial Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属山东省立医院）
Despite high global prevalence of hepatic steatosis, no automated diagnostics demonstrated generalizability in detecting steatosis on multiple international datasets. Traditionally, hepatic steatosis detection relies on clinicians selecting the region of interest (ROI) on computed tomography (CT) to measure liver attenuation. ROI selection demands time and expertise, and therefore is not routinely performed in populations. To automate the process, we validated an existing artificial intelligence (AI) system for 3D liver segmentation and used it to purpose a novel method: AI-ROI, which could au…
Automated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exist, involving sophisticated pipelines trained and validated on different datasets. However, the clinical applicability of these approaches across diseases remains limited. We compared four generic deep learning approaches trained on various datasets and two readily available lung segmentation algorithms. We performed evaluation on routine imaging data with more than six different disease patterns and three published data sets. Using diffe…
Department of Surgical Sciences, Uppsala University（乌普萨拉大学外科学系）；Department of Information Technology, Uppsala University（乌普萨拉大学信息技术系）；Antaros Medical AB
UK Biobank (UKB) conducts large-scale examinations of more than half a million volunteers, collecting health-related information on genetics, lifestyle, blood biochemistry, and more. Medical imaging of 100,000 subjects, with 70,000 follow-up sessions, enables measurements of organs, muscle, and body composition. With up to 170,000 mounting MR images, various methodologies are accordingly engaged in large-scale image analysis. This work presents an experimental inference engine that can automatically predict a comprehensive profile of subject metadata from UKB neck-to-knee body MRI. It was eval…
Radboud University Medical Center（拉德堡德大学医学中心）；GZA-ZNA Hospitals（GZA-ZNA医院）；Peter Mac Callum Cancer Centre（彼得·麦卡勒姆癌症中心）；Mayo Clinic（梅奥诊所）；Medipath & American Hospital of Paris（Medipath与巴黎美国医院）；Canisius Wilhelmina Ziekenhuis（卡尼修斯·威廉米娜医院）；University of Porto（波尔图大学）；The Netherlands Cancer Institute（荷兰癌症研究所）；Technical University Munich（慕尼黑工业大学）；Hannover Medical School（汉诺威医学院）
Nuclear pleomorphism, defined herein as the extent of abnormalities in the overall appearance of tumor nuclei, is one of the components of the three-tiered breast cancer grading. Given that nuclear pleomorphism reflects a continuous spectrum of variation, we trained a deep neural network on a large variety of tumor regions from the collective knowledge of several pathologists, without constraining the network to the traditional three-category classification. We also motivate an additional approach in which we discuss the additional benefit of normal epithelium as baseline, following the routin…
Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；University Heidelberg（海德堡大学）；Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院与大学医院）；Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”（那不勒斯国家肿瘤研究所IRCCS“G.帕斯卡莱”）
Surgical resections are the most prevalent curative treatment for primary liver cancer. Tumors located in critical positions are known to complexify liver resections (LR). While experienced surgeons in specialized medical centers may have the necessary expertise to accurately anticipate LR complexity, and prepare accordingly, an objective method able to reproduce this behavior would have the potential to improve the standard routine of care, and avoid intra- and postoperative complications. In this article, we propose CoRe, an automated medical image processing pipeline for the prediction of p…
Coronary artery disease (CAD) remains the world's leading cause of mortality and the disease burden is continually expanding as the population ages. Recently, the MR-INFORM randomised trial has demonstrated that the management of patients with stable CAD can be guided by stress perfusion cardiovascular magnetic resonance (CMR) imaging and it is non-inferior to the using the invasive reference standard of fractional flow reserve. The benefits of using stress perfusion CMR include that it is non-invasive and significantly reduces the number of unnecessary coronary revascularisations. As compared…
