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 order to account for inter-observer variability, without sacrificing accuracy, we propose a novel variational inference framework to model the distribution of plausible segmentation maps, given a specific MR image, which explicitly represents the multi-reader variability. Specifically, we resort to a latent vector to encode the multi-reader variability and counteract the inherent information loss in the imaging data. Then, we apply a variational autoencoder network and optimize its evidence lower bound (ELBO) to efficiently approximate the distribution of the segmentation map, given an MR image. Experimental results, carried out with the QUBIQ brain growth MRI segmentation datasets with seven annotators, demonstrate the effectiveness of our approach.
Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation
University of Lausanne（洛桑大学）；Lausanne University Hospital（洛桑大学医院）；HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland（瑞士西部高等专业学院瓦莱州瓦利斯分校）；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；ALTA Institute, University of Cambridge（剑桥大学ALTA研究所）；Yandex Research（Yandex研究院）；Isomorphic Labs（同构实验室）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；University Hospital Basel and University of Basel（巴塞尔大学医院与巴塞尔大学）
WASABI: A Metric for Evaluating Morphometric Plausibility of Synthetic Brain MRIs
Department of Radiology, Weill Cornell Medicine, New York, NY, USA（放射科，韦尔·科恩医学中心，纽约，纽约州，美国）；Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA（精神病学与行为科学系，斯坦福大学，斯坦福，加利福尼亚州，美国）；Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA（电气与计算机工程系，康奈尔大学，伊萨卡，纽约州，美国）
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
Imperial College London（帝国理工学院）；MRC London Clinical Sciences Centre（MRC伦敦临床科学中心）；National Heart & Lung Institute（国家心肺研究所）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学研究生医学院）
Female-RHINO: A Real-Time Scanner-Integrated Framework for Automated Quantitative Uterine MRI Analysis and Structured Reporting
Radiologisches Institut, Universitätsklinikum Erlangen（埃朗根大学医院放射学研究所）；School of Biomedical Engineering and Imaging, KCL（KCL生物医学工程与成像学院）；Institute for Information Processing, Leibniz University Hannover（汉诺威莱布尼茨大学信息处理研究所）；CAIMed, L3S research institute（CAIMed，L3S研究机构）
CheXmask-U: Quantifying uncertainty in landmark-based anatomical segmentation for X-ray images
Laboratory of Applied Artificial Intelligence, Institute of Computer Sciences, CONICET - Universidad de Buenos Aires（应用人工智能实验室，计算机科学研究所，CONICET - 华盛顿大学布宜诺斯艾利斯分校）；Weizmann Institute of Science（科学研究所）；APOLO Biotech（APOLO生物技术）；Research Institute for Signals, Systems and Computational Intelligence, sinc(i), CONICET - Universidad Nacional del Litoral（信号、系统和计算智能研究 institute，sinc(i)，CONICET - 沿海国立大学）
Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images
University Medical Center Utrecht（乌得勒支大学医学中心）；Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹医学中心）；University of Twente（特文特大学）；Wageningen University（瓦赫宁根大学）
SGRNet: Spatially Guided Radiology Network for Structured Radiological Reporting of Head and Neck Cancer
University of Strasbourg（斯特拉斯堡大学）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；Indian Institute of Technology (IIT) Madras（印度马德拉斯理工学院）；All India Institute of Medical Sciences（全印度医学科学院）
Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss
University of Calgary（卡尔加里大学）；McCaig Institute for Bone and Joint Health（麦凯格骨骼与关节健康研究所）；University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute (AMII)（阿尔伯塔机器智能研究所）
Quality analysis of DCGAN-generated mammography lesions
Computer Vision and Robotics Institute, University of Girona（赫罗纳大学计算机视觉与机器人研究所）；University of Barcelona（巴塞罗那大学）；Faculty of Medicine, University of Girona（赫罗纳大学医学院）；Parc Tauli Hospital Universitari（陶利公园大学医院）；Institut d’Investigacio Parc Tauli I3PT（陶利公园研究所）
Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection
Duke University（杜克大学）；Duke Kunshan University（昆山杜克大学）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；The University of Texas MD Anderson Graduate School of Biomedical Science（德克萨斯大学MD安德森生物医学研究生院）
Advances in Kidney Biopsy Lesion Assessment through Dense Instance Segmentation
Leiden University（莱顿大学）；Leiden University Medical Center（莱顿大学医学中心）；Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学医学中心（阿姆斯特丹大学））；University Medical Center Utrecht（乌得勒支大学医学中心）；University Hospitals Leuven（鲁汶大学医院）；KU Leuven（鲁汶大学）；Van’t Hoff Institute for Molecular Sciences, University of Amsterdam（阿姆斯特丹大学范特霍夫分子科学研究所）
Changes in Visual Attention Patterns for Detection Tasks due to Dependencies on Signal and Background Spatial Frequencies
Department of Biomedical Engineering, University of Houston（生物医学工程系，休斯顿大学）；Department of Physics, University of Houston（物理系，休斯顿大学）；Department of Electrical and Computer Engineering, University of Houston（电气与计算机工程系，休斯顿大学）
Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model
College of Computer Science, Sichuan University（四川大学计算机学院）；Key Laboratory of Data Protection and Intelligent Management, Ministry of Education, Sichuan University（四川大学（教育部数据保护与智能管理重点实验室））；School of Cyber Science and Engineering, Sichuan University（四川大学网络空间安全学院）；School of Computer Science and Software Engineering, Southwest Petroleum University（西南石油大学计算机科学与技术学院）；Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
SonoCLIP: Mask-Guided Region-Aware Vision-Language Pretraining for Fetal Ultrasound Analysis
School of Computer Science, Wuhan University, Wuhan, China（武汉大学计算机学院，武汉，中国）；Institute of Artificial Intelligence, Wuhan University, Wuhan, China（武汉大学人工智能研究院，武汉，中国）；Department of Ultrasound, Renmin Hospital of Wuhan University, Wuhan, China（武汉大学仁民医院超声科，武汉，中国）；National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China（武汉大学多媒体软件国家工程研究中心，武汉，中国）；Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China（湖北省多媒体与网络通信工程重点实验室，武汉大学，武汉，中国）
Automated Histopathologic Assessment of Hirschsprung Disease Using a Multi-Stage Vision Transformer Framework
Department of Systems and Computer Engineering, Carleton University（系统与计算机工程系，卡尔顿大学）；Department of Clinical Science and Translational Medicine, University of Ottawa（临床科学与转化医学系，渥太华大学）；School of Epidemiology and Public Health, University of Ottawa（流行病学与公共卫生学院，渥太华大学）；Department of Methodological and Implementation Research, Ottawa Hospital Research Institute（方法学与实施研究系，渥太华医院研究所）；Children’s Hospital of Eastern Ontario (CHEO)（东部 Ontario儿童医院（CHEO））
Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning
National University of Cordoba（科尔多瓦国立大学）；Technical University of Munich（慕尼黑工业大学）；University of Burgundy（勃艮第大学）；University Hospital of Dijon（第戎大学医院）；Catholic University of Cordoba（科尔多瓦天主教大学）
Learning Numerical Observers using Unsupervised Domain Adaptation
Carle Cancer Center, Carle Foundation Hospital（卡勒癌症中心，卡勒基金会医院）；Department of Bioengineering, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校生物工程系）
Uncertainty-driven ensembles of deep architectures for multiclass classification. Application to COVID-19 diagnosis in chest X-ray images
Elsevier Science Publishers B.V.（爱思唯尔科学出版公司）；University of Winchester（温彻斯特大学）；Universidad de Granada（格拉纳达大学）；Universidad de Malaga（马拉加大学）；University of Leicester（莱斯特大学）
Autopet III challenge: Incorporating anatomical knowledge into nnUNet for lesion segmentation in PET/CT
Institute for AI in Medicine (IKIM)（医学人工智能研究所）；University Hospital Essen (AöR)（埃森大学医院）；Department of Nuclear Medicine（核医学科）；Cancer Research Center Cologne Essen (CCCE)（科隆埃森癌症研究中心）；West German Cancer Center Essen University Hospital Essen (AöR)（埃森大学医院西德癌症中心）；TU Dortmund（多特蒙德工业大学）；German Cancer Consortium (DKTK, Partner site Essen)（德国癌症联盟（埃森合作站点））
本研究针对 PET/CT 病变分割，提出基于示踪剂分类和集成 nnUNet 的方法，融合解剖标签，在 autoPET III 挑战中取得高 Dice 分数。
Generation of Heterogeneous PET Images from Uniform Organ Activity Maps Using a Pretrained Domain-Adapted Diffusion Model
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, USA（华盛顿大学医学院马林克罗德特放射医学研究所，圣路易斯，美国）；Imaging Science Program, McKelvey School of Engineering, Washington University in St Louis, St. Louis, USA（华盛顿大学圣路易斯分校麦克雷高中工程学院成像科学计划，圣路易斯，美国）；Department of Surgery, Washington University School of Medicine, St. Louis, USA（华盛顿大学医学院外科部，圣路易斯，美国）；Department of Biomedical Engineering, Washington University in St Louis, St. Louis, USA（华盛顿大学圣路易斯分校生物医学工程部，圣路易斯，美国）
Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks
Imperial College London（帝国理工学院）；ETH Zurich（苏黎世联邦理工学院）；Guy’s and St Thomas’ NHS Foundation（盖伊和圣托马斯国民保健服务基金会）
从胎儿超声图像中测量头部生物特征对于监测胎儿的健康发育具有重要意义。然而，相关解剖结构的准确测量在临床中受到较大的观察者间差异的影响。为了解决这个问题，提出了一种利用全卷积网络（FCN）来确定胎儿头围（HC）和双顶径（BPD）测量值的自动化方法。在常规筛查检查期间，在头部的大约2000个2D超声图像上训练FCN，所述图像具有由45个不同的超声医师提供的注释，以执行头部的语义分割。椭圆被拟合到所得到的分割轮廓，以模仿通常由声谱仪操作员产生的注释。该模型的性能与观察者间的变异性进行了比较，其中两位专家手动注释了100张测试图像。HC的平均绝对模型专家误差略优于观察者间误差（1.99 mm vs 2.16 mm），BPD（0.61 mm vs 0.59 mm）以及Dice系数（0.980 vs 0.980）相当。我们的研究结果表明，该模型的性能与人类专家相似，并学会从由许多超声医师注释的大型数据集中产生准确的预测。此外，在GPU上以15 fps的速度近实时生成测量结果，这可以加快熟练和实习超声医师的临床工作流程。
Echocardiography Segmentation with Enforced Temporal Consistency
University of Sherbrooke（舍布鲁克大学）；Univ Lyon（里昂大学）；Université Claude Bernard Lyon 1（里昂第一大学）；UJM-Saint Etienne（圣艾蒂安大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；Institut Universitaire de France（法国大学研究院）
Unsupervised Anomaly Detection of Diseases in the Female Pelvis for Real-Time MR Imaging
Radiological Institute, University Hospital Erlangen (UKER)（放射医学研究所，埃尔兰根大学医院（UKER））；Institute of Information Processing, Leibniz University Hannover（信息处理研究所，汉诺威莱布尼茨大学）；Image Data Exploration and Analysis Lab, Friedrich-Alexander University Erlangen–Nürnberg (FAU)（图像数据探索与分析实验室，埃尔兰根-纽伦堡弗里德里希-亚历山大大学（FAU））；Institute of Women’s Health, UKER（妇女健康研究所，UKER）
