The longitudinal modeling of neuroanatomical changes related to Alzheimer's disease (AD) is crucial for studying the progression of the disease. To this end, we introduce TransforMesh, a spatio-temporal network based on transformers that models longitudinal shape changes on 3D anatomical meshes. While transformer and mesh networks have recently shown impressive performances in natural language processing and computer vision, their application to medical image analysis has been very limited. To the best of our knowledge, this is the first work that combines transformer and mesh networks. Our results show that TransforMesh can model shape trajectories better than other baseline architectures that do not capture temporal dependencies. Moreover, we also explore the capabilities of TransforMesh in detecting structural anomalies of the hippocampus in patients developing AD.
From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation
Gran Sasso Science Institute (GSSI)（大萨索科学研究所）；European Hospital（欧洲医院）；University of Edinburgh（爱丁堡大学）；Physics of Fluids Group, University of Twente（特温特大学流体物理小组）；INFN–Laboratori Nazionali del Gran Sasso（意大利国家核物理研究所大萨索国家实验室）
Lab for Artificial Intelligence in Medical Imaging (AI-Med), KJP, LMU M\"unchen, Germany
Geometric deep learning can find representations that are optimal for a given task and therefore improve the performance over pre-defined representations. While current work has mainly focused on point representations, meshes also contain connectivity information and are therefore a more comprehensive characterization of the underlying anatomical surface. In this work, we evaluate four recent geometric deep learning approaches that operate on mesh representations. These approaches can be grouped into template-free and template-based approaches, where the template-based methods need a more elab…
AdamFlow: Adam-based Wasserstein Gradient Flows for Surface Registration in Medical Imaging
Department of Brain Sciences, Imperial College London（帝国理工学院脑科学系）；Department of Computing, Imperial College London（帝国理工学院计算系）；School of Computer Science, University of Birmingham（伯明翰大学计算机科学学院）；Department of Computer Science, Columbia University（哥伦比亚大学计算机科学系）；Data Science Institute, Imperial College London（帝国理工学院数据科学研究所）
Graph neural networks have emerged as a promising approach for the analysis of non-Euclidean data such as meshes. In medical imaging, mesh-like data plays an important role for modelling anatomical structures, and shape classification can be used in computer aided diagnosis and disease detection. However, with a plethora of options, the best architectural choices for medical shape analysis using GNNs remain unclear. We conduct a comparative analysis to provide practitioners with an overview of the current state-of-the-art in geometric deep learning for shape classification in neuroimaging. Usi…
Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI
Institute for Signals, Systems, and Computational Intelligence, sinc(i) CONICET-UNL（信号、系统与计算智能研究所，sinc(i) CONICET-UNL）；Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds（生物医学计算成像与模拟技术中心（CISTIB），计算学院，莱斯特大学）；Christabel Pankhurst Institute, The University of Manchester（克里斯特贝尔·潘克赫斯特研究所，曼彻斯特大学）；Centre for Computational Imaging and Modelling in Medicine (CIMIM), Department of Computer Science, School of Engineering, and Division of Informatics Imaging and Data Science, School of Health Sciences, The University of Manchester（医学计算成像与建模中心（CIMIM），计算机科学系，工程学院，信息学成像与数据科学系，健康科学学院，曼彻斯特大学）；Medical Imaging Research Centre (MIRC), Department of Cardiovascular Sciences, KU Leuven（医学影像研究中心（MIRC），心血管科学系，鲁汶大学）
Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA（计算与增强智能学院，亚利桑那州立大学）；Amazon AGI, Redmond, WA, USA（亚马逊人工智慧部门）；Banner Alzheimer’s Institute, Phoenix, AZ, USA（贝纳尔德阿尔茨海默病研究所）
Medical Image Analysis Lab, School of Computing Science（计算科学学院医学图像分析实验室）；Simon Fraser University（西蒙弗雷泽大学）
We present an automated approach to detect and longitudinally track skin lesions on 3D total-body skin surface scans. The acquired 3D mesh of the subject is unwrapped to a 2D texture image, where a trained objected detection model, Faster R-CNN, localizes the lesions within the 2D domain. These detected skin lesions are mapped back to the 3D surface of the subject and, for subjects imaged multiple times, we construct a graph-based matching procedure to longitudinally track lesions that considers the anatomical correspondences among pairs of meshes and the geodesic proximity of corresponding le…
Neural Implicit Heart Coordinates: 3D cardiac shape reconstruction from sparse segmentations
School of Biomedical Engineering and Imaging Sciences（生物医学工程与成像科学学院）；William Harvey Research Institute, NIHR Barts Biomedical Research Centre（威廉·哈里森研究机构、NIHR巴茨生物医学研究中心）；Simula Research Laboratory（Simula研究实验室）；Department of Informatics（信息学院）；Department of Bioengineering（生物工程学院）；Barts Heart Centre, Barts Health NHS Trust（巴茨心脏中心、巴茨健康 NHS信托）；Technical University of Munich（慕尼黑技术大学）
Shape Deformation Networks for Automated Aortic Valve Finite Element Meshing from 3D CT Images
Department of Computer Science, University of Miami（计算机科学系，迈阿密大学）；Sutra Medical Inc（Sutra医疗公司）；Department of Mechanical Engineering, Texas Tech University（机械工程系，德克萨斯技术大学）
Statistical Hand Shape Modeling from Clinical CT Scans Using Deep Learning and Implicit Skinning
Dept. of Computer Science and Engineering（计算机科学与工程系）；Özyeğin University（奥兹耶尼大学）；Dept. of Artificial Intelligence and Data Engineering（人工智能与数据工程系）；Osteoid Inc.（Osteoid公司）
MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction
School of Biomedical Engineering and Imaging Sciences, King’s College London（生物医学工程与影像科学学院，伦敦国王学院）；Brain Imaging & Neuro Epidemiology Department, Luxemboug Institute of Health（脑影像与神经流行病学部门，卢森堡健康研究所）；Department of Biostatistics and Health Informatics, King’s College London（生物统计学与健康信息学系，伦敦国王学院）
Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation
Center for Virtual Imaging Trials, Department of Radiology, Duke University School of Medicine, Durham, NC, USA Biomedical Imaging Group Rotterdam, Department of Radiology \& Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands Electrical；Computer Engineering, Pratt School of Engineering, Duke University, Durham, NC, USA
