Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models
Technical University of Denmark（丹麦技术大学）；University of Copenhagen（哥本哈根大学）；Pioneer Centre for AI（人工智能先锋中心）；Rigshospitalet（哥本哈根大学医院）
Markus Ditlev Sjøgren Olsen, Jakob Ambsdorf, Manxi Lin, Caroline Taksøe-Vester, Morten Bo Søndergaard Svendsen, Anders Nymark Christensen, Mads Nielsen, Martin … 展开作者
Markus Ditlev Sjøgren Olsen, Jakob Ambsdorf, Manxi Lin, Caroline Taksøe-Vester, Morten Bo Søndergaard Svendsen, Anders Nymark Christensen, Mads Nielsen, Martin Grønnebæk Tolsgaard, Aasa Feragen, Paraskevas Pegios
Congenital malformations of the brain are among the most common fetal abnormalities that impact fetal development. Previous anomaly detection methods on ultrasound images are based on supervised learning, rely on manual annotations, and risk missing underrepresented categories. In this work, we frame fetal brain anomaly detection as an unsupervised task using diffusion models. To this end, we employ an inpainting-based Noise Agnostic Anomaly Detection approach that identifies the abnormality using diffusion-reconstructed fetal brain images from multiple noise levels. Our approach only requires normal fetal brain ultrasound images for training, addressing the limited availability of abnormal data. Our experiments on a real-world clinical dataset show the potential of using unsupervised methods for fetal brain anomaly detection. Additionally, we comprehensively evaluate how different noise types affect diffusion models in the fetal anomaly detection domain.
Congenital heart disease is considered as one the most common groups of congenital malformations which affects $6-11$ per $1000$ newborns. In this work, an automated framework for detection of cardiac anomalies during ultrasound screening is proposed and evaluated on the example of Hypoplastic Left Heart Syndrome (HLHS), a sub-category of congenital heart disease. We propose an unsupervised approach that learns healthy anatomy exclusively from clinically confirmed normal control patients. We evaluate a number of known anomaly detection frameworks together with a model architecture based on the…
Multi-Center Study on Deep Learning-Assisted Detection and Classification of Fetal Central Nervous System Anomalies Using Ultrasound Imaging
School of Mathematics and Statistics, Xiamen University of Technology（厦门理工学院数学与统计学院）；Department of Obstetrics, Xiamen University Affiliated First Hospital（厦门大学附属第一医院妇产科）
Origin Health India（印度Origin Health公司）；Bangalore Fetal Medicine Center（班加罗尔胎儿医学中心）；Prime Imaging and Prenatal Diagnostics（Prime影像与产前诊断中心）；Kasturba Medical College（卡斯图尔巴医学院）；Manipal Academy of Higher Education（马尼帕尔高等教育学院）；Origin Health（Origin Health公司）
Multiple studies have demonstrated that obtaining standardized fetal brain biometry from mid-trimester ultrasonography (USG) examination is key for the reliable assessment of fetal neurodevelopment and the screening of central nervous system (CNS) anomalies. Obtaining these measurements is highly subjective, expertise-driven, and requires years of training experience, limiting quality prenatal care for all pregnant mothers. In this study, we propose a deep learning (DL) approach to compute 3 key fetal brain biometry from the 2D USG images of the transcerebellar plane (TC) through the accurate …
IBM Research – Almaden Research Center（IBM阿尔马登研究中心）；Eastern Virginia Medical School（东弗吉尼亚医学院）
Congenital heart disease (CHD) is the most common congenital abnormality associated with birth defects in the United States. Despite training efforts and substantial advancement in ultrasound technology over the past years, CHD remains an abnormality that is frequently missed during prenatal ultrasonography. Therefore, computer-aided detection of CHD can play a critical role in prenatal care by improving screening and diagnosis. Since many CHDs involve structural abnormalities, automatic segmentation of anatomical structures is an important step in the analysis of fetal echocardiograms. While …
Origin Health India（印度Origin Health）；Bangalore Fetal Medicine Center（班加罗尔胎儿医学中心）；Prime Imaging and Prenatal Diagnostics（Prime影像与产前诊断中心）；Kasturba Medical College, Manipal Academy of Higher Education（马尼帕尔高等教育学院卡斯特尔巴医学院）；Origin Health；Universiti Malaya（马来亚大学）；Aarthi Scans and Labs（Aarthi扫描与实验室）
Quality assessment of prenatal ultrasonography is essential for the screening of fetal central nervous system (CNS) anomalies. The interpretation of fetal brain structures is highly subjective, expertise-driven, and requires years of training experience, limiting quality prenatal care for all pregnant mothers. With recent advancement in Artificial Intelligence (AI), specifically deep learning (DL), assistance in precise anatomy identification through semantic segmentation essential for the reliable assessment of growth and neurodevelopment, and detection of structural abnormalities have been p…
Fetal alcohol syndrome (FAS) caused by prenatal alcohol exposure can result in a series of cranio-facial anomalies, and behavioral and neurocognitive problems. Current diagnosis of FAS is typically done by identifying a set of facial characteristics, which are often obtained by manual examination. Anatomical landmark detection, which provides rich geometric information, is important to detect the presence of FAS associated facial anomalies. This imaging application is characterized by large variations in data appearance and limited availability of labeled data. Current deep learning-based heat…
Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review
Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg（卢森堡大学生命科学与医学系，科学、技术与医学学院）；Department of Biostatistics and Medical Informatics, Institute of Health Sciences, Ege University（伊兹密尔大学健康科学学院生物统计学与医学信息学系）
Generating healthy counterfactuals with denoising diffusion bridge models
Athinoula A. Martinos Center for Biomedical Imaging（阿提诺拉A·马丁诺生物医学成像中心）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；ETH Zurich（苏黎世联邦理工学院）；Computer Science & Artificial Intelligence Lab（计算机科学与人工智能实验室）；Hawkes Institute（哈维斯研究所）；University College London（伦敦大学学院）
King’s College London（伦敦国王学院）；University College London（伦敦大学学院）；Birkbeck College（伯克贝克学院）；King’s College Hospital NHS Foundation Trust（国王学院医院国民保健服务基金会信托）；St George’s University Hospitals NHS Foundation Trust（圣乔治大学医院国民保健服务基金会信托）；Atkinson Morley Regional Neuroscience Centre（阿特金森·莫利地区神经科学中心）；UCL Queen Square Institute of Neurology（伦敦大学学院皇后广场神经病学研究所）
Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive transformers have achieved state-of-the-art performance for anomaly detection in medical imaging. Nonetheless, these models still have some intrinsic weaknesses, such as requiring images to be modelled as 1D sequences, the accumulation of errors during the sampling process, and the significant inference times associated with transformers. Denoising diffusion probabilistic models are a class of non-autoregressive generativ…
OXSeg: Multidimensional attention UNet-based lip segmentation using semi-supervised lip contours
Nuffield Department of Women’s & Reproductive Health, University of Oxford（牛津大学妇女与生殖健康系）；Big Data Institute, University of Oxford（牛津大学大数据研究所）；Department of Pediatrics, University of California San Diego（加州大学圣地亚哥分校儿科系）；Department of Psychology, Center for Behavioral Teratology, San Diego State University（圣地亚哥州立大学心理学系）；University of Minnesota Twin Cities（明尼苏达大学双城分校）；Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine（埃默里大学医学院精神病学与行为科学系）；Faculty of Health and Medical Science, University of Surrey Medical School（萨里大学医学与健康科学学院）
Longitudinal fetal brain atlas is a powerful tool for understanding and characterizing the complex process of fetus brain development. Existing fetus brain atlases are typically constructed by averaged brain images on discrete time points independently over time. Due to the differences in onto-genetic trends among samples at different time points, the resulting atlases suffer from temporal inconsistency, which may lead to estimating error of the brain developmental characteristic parameters along the timeline. To this end, we proposed a multi-stage deep-learning framework to tackle the time in…
Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control
Department of Applied Statistics & Operations Research (ASOR), Bowling Green State University, Bowling Green, OH, USA（应用统计与运筹学系，布罗金格林州立大学）；Data Science Department, Actual Reality Technologies, OH, USA（数据科学系，Actual Reality Technologies）
Canon Medical Research Europe（佳能医疗欧洲研究中心）；University of Glasgow（格拉斯哥大学）；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection perform…
Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound
Medical Ultrasound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, China The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China Centre for CIMIM, Manchester University, Manchester, UK School of Artificial Intelligence, Shenzhen University, Shenzhen, China National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China School of Biomedical Engineering；Informatics, Nanjing Medical University, Nanjing, China
Scalable AI Framework for Defect Detection in Metal Additive Manufacturing
Polaron Analytics（极化子分析公司）；University of Dayton Research Institute（代顿大学研究院）；University of Dayton（代顿大学）；NASA Marshall Space Flight Center（美国国家航空航天局马歇尔太空飞行中心）；National Aeronautics and Space Administration（美国国家航空航天局）
A Unified Score Matching Paradigm for Video Anomaly Detection and Anticipation
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（空天地海一体化大数据应用技术国家工程实验室）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息学院）
Self-Supervised Ultrasound Representation Learning for Renal Anomaly Prediction in Prenatal Imaging
organization= Department of Systems；Computer Engineering, Carleton University , city= Ottawa , state= Ontario , country= Canada；organization= Department of Methodological；Implementation Research, Ottawa Hospital Research Institute , city= Ottawa , state= Ontario , country= Canada；organization= Department of Acute Care Research, Ottawa Hospital Research Institute , city= Ottawa , state= Ontario , country= Canada；organization= Children's Hospital of Eastern Ontario Research Institute , city= Ottawa , state= Ontario , country= Canada；organization= Better Outcomes Registry \& Network Ontario, Children’s Hospital of Eastern , city= Ottawa , state= Ontario , country= Canada；organization= Department of Obstetrics；Gynecology, University of Ottawa , city= Ottawa , state= Ontario , country= Canada；organization= School of Epidemiology；Public Health, University of Ottawa , city= Ottawa , state= Ontario , country= Canada；organization= Department of Obstetrics, Gynecology \& Newborn Care, The Ottawa Hospital , city= Ottawa , state= Ontario , country= Canada；Global Health Office, University of Ottawa , city= Ottawa , state= Ontario , country= Canada；organization= Department of Clinical Science；Translational Medicine, University of Ottawa , city= Ottawa , state= Ontario , country= Canada
Blind Ultrasound Image Enhancement via Self-Supervised Physics-Guided Degradation Modeling
Department of Computer Engineering, College of Computing and Mathematics, King Fahd University of Petroleum & Minerals (KFUPM)（计算机工程系，计算与数学学院，国王法赫德石油与矿物大学）；SDAIA–KFUPM Joint Research Center for Artificial Intelligence (JRC-AI), King Fahd University of Petroleum & Minerals（人工智能联合研究中心（JRC-AI），国王法赫德石油与矿物大学）；Department of Computer Science and Information Technology, Sir Syed University of Engineering and Technology (SSUET)（计算机科学与信息科技系，西德大学工程与技术学院）；Digital Technology & Innovation Center, Siemens Healthineers, Princeton, NJ 08540, USA（数字技术与创新中心，西门子健康部，美国新泽西州普林斯顿）；Department of Aerospace Engineering, King Fahd University of Petroleum & Minerals (KFUPM)（航空航天工程系，国王法赫德石油与矿物大学）；Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals (KFUPM)（智能交通与物流跨学科研究中心，国王法赫德石油与矿物大学）
