3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. Today fully-convolutional networks (FCNs), while dominant, do not offer these capabilities. We present deep implicit statistical shape models (DISSMs), a new approach to delineation that marries the representation power of convolutional neural networks (CNNs) with the robustness of SSMs. DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance. To reliably fit anatomically plausible shapes to an image, we introduce a novel rigid and non-rigid pose estimation pipeline that is modelled as a Markov decision process(MDP). We outline a training regime that includes inverted episodic training and a deep realization of marginal space learning (MSL). Intra-dataset experiments on the task of pathological liver segmentation demonstrate that DISSMs can perform more robustly than three leading FCN models, including nnU-Net: reducing the mean Hausdorff distance (HD) by 7.7-14.3mm and improving the worst case Dice-Sorensen coefficient (DSC) by 1.2-2.3%. More critically, cross-dataset experiments on a dataset directly reflecting clinical deployment scenarios demonstrate that DISSMs improve the mean DSC and HD by 3.5-5.9% and 12.3-24.5mm, respectively, and the worst-case DSC by 5.4-7.3%. These improvements are over and above any benefits from representing delineations with high-quality surface.
Detection of curved lines with B-COSFIRE filters: A case study on crack delineation
Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen（格罗宁根大学约翰·伯努利数学与计算机科学研究所）；Intelligent Computer Systems, University of Malta（马耳他大学智能计算机系统研究所）
Implicit Deformable Medical Image Registration with Learnable Kernels
Department of Computer Science Interactive Graphics and Simulation Group (IGS), University of Innsbruck, Technikerstraße 21 Innsbruck, Austria（因计算机科学系交互式图形与模拟组（IGS），因斯布鲁克大学）；Interventional Oncology-Microinvasive Therapy (SIP), Department of Radiology, Medical University Innsbruck, Innsbruck, Austria（介入肿瘤学-微侵袭疗法（SIP），放射学系，因斯布鲁克医学院）
End-to-End Learning of Multi-Organ Implicit Surfaces from 3D Medical Imaging Data
University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France Institute of Image-Guided Surgery, IHU Strasbourg, Strasbourg, France Department of Diagnostic Radiology, McGill University, Montreal, Canada Augmented Intelligence \& Precision Health Laboratory (AIPHL), McGill University Health Centre Research Institute, Montreal, Canada
Implicit Neural Representation in Medical Imaging: A Comparative Survey
Iran University of Science and Technology（伊朗科技大学）；South Dakota State University（南达科他州立大学）；RWTH Aachen University（亚琛工业大学）；University of Regensburg（雷根斯堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）
ProSona: Prompt-Guided Personalization for Multi-Expert Medical Image Segmentation
University Hospital Bonn（波恩大学医院）；LMU University Hospital（慕尼黑莱茵河医学院医院）；University Hospital Düsseldorf（杜伊斯堡大学医院）；Heidelberg University Hospital（海德堡大学医院）；Università Cattolica del Sacro Cuore（罗马天主教圣心大学）；University Hospital Zurich（苏黎世大学医院）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；DTU Compute（技术大学计算学院）；Helmholtz AI（海德堡中心人工智能）
Deep vessel segmentation with joint multi-prior encoding
LaTIM UMR 1101；Inserm（法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）；University Hospital of Brest（布雷斯特大学医院）；CONICET（阿根廷国家科学技术研究委员会）；ICube UMR 7357；CNRS（法国国家科学研究中心）；IMT Atlantique（大西洋电信学院）
Hyper-Convolutions via Implicit Kernels for Medical Imaging
Cornell University（康奈尔大学）；Cornell Tech（康奈尔科技学院）；Massachusetts Institute of Technology（麻省理工学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Hupan Lab（湖畔实验室）；College of Computer Science and Technology, Zhejiang University, China（浙江大学计算机科学与技术学院）；Shengjing Hospital of China Medical University, China（中国医科大学附属盛京医院）
ViPSAM: Visual Prompting Medical Image Segmentation Using Segment Anything Model
Department of Artificial Intelligence, Sungkyunkwan University, Republic of Korea（人工智能系，成均馆大学，大韩民国）；Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea（放射肿瘤科，三星医疗中心，成均馆大学医学院，大韩民国）；Department of MetaBioHealth, Sungkyunkwan University, Republic of Korea（元生物健康系，成均馆大学，大韩民国）
RibPull: Implicit Occupancy Fields and Medial Axis Extraction for CT Ribcage Scans
Electrical and Computer Engineering Department, University of California, Santa Cruz（加州大学圣克ruz分校电子与计算机工程系）；Inria, Univ. Rennes, CNRS, IRISA, M2S, Rennes（Inria，里尔大学，CNRS，IRISA，M2S，里尔）；LIRIS - CNRS - Université Claude Bernard Lyon 1（LIRIS-CNRS-里尔1大学）；Computer Science Engineering Department, University of California, Santa Cruz（加州大学圣克ruz分校计算机科学工程系）
Detailed delineation of the fetal brain in diffusion MRI via multi-task learning
Harvard Medical School（哈佛医学院）；Elmhurst Hospital Center（埃尔姆赫斯特医院中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Massachusetts General Hospital（麻省总医院）；University of California Irvine（加州大学欧文分校）
CFFormer: Cross CNN-Transformer Channel Attention and Spatial Feature Fusion for Improved Segmentation of Heterogeneous Medical Images
School of Computer Science, University of Nottingham Ningbo China（Nottingham 中国 Nottingham 理工学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China（深圳先进技术研究所，中国科学院）；School of Mathematical and Computational Sciences, Massey University（数学与计算科学学院，马斯sey 大学）；University of Nottingham, United Kingdom（Nottingham 大学，英国）
Adapting Segment Anything Model 3 for Concept-Driven Lesion Segmentation in Medical Images: An Experimental Study
The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心放射肿瘤学系医学人工智能与自动化（MAIA）实验室）；Medical Image Processing Group, Department of Radiology, University of Pennsylvania（宾夕法尼亚大学放射学系医学图像处理组）；Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心内科学系消化与肝脏疾病科）
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning
The University of Texas Arlington（德克萨斯大学阿灵顿分校）；RIKEN（理化学研究所）；University of Tokyo（东京大学）；National Cancer Center Research Institute（国立癌症中心研究所）；National Library of Medicine - National Institutes of Health（美国国立卫生研究院国家医学图书馆）；Temple University（天普大学）
