We present MedShapeNet, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D surgical instrument models. Prior to the deep learning era, the broad application of statistical shape models (SSMs) in medical image analysis is evidence that shapes have been commonly used to describe medical data. Nowadays, however, state-of-the-art (SOTA) deep learning algorithms in medical imaging are predominantly voxel-based. In computer vision, on the contrary, shapes (including, voxel occupancy grids, meshes, point clouds and implicit surface models) are preferred data representations in 3D, as seen from the numerous shape-related publications in premier vision conferences, such as the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), as well as the increasing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models) in computer vision research. MedShapeNet is created as an alternative to these commonly used shape benchmarks to facilitate the translation of data-driven vision algorithms to medical applications, and it extends the opportunities to adapt SOTA vision algorithms to solve critical medical problems. Besides, the majority of the medical shapes in MedShapeNet are modeled directly on the imaging data of real patients, and therefore it complements well existing shape benchmarks comprising of computer-aided design (CAD) models. MedShapeNet currently includes more than 100,000 medical shapes, and provides annotations in the form of paired data. It is therefore also a freely available repository of 3D models for extended reality (virtual reality - VR, augmented reality - AR, mixed reality - MR) and medical 3D printing. This white paper describes in detail the motivations behind MedShapeNet, the shape acquisition procedures, the use cases, as well as the usage of the online shape search portal: https://medshapenet.ikim.nrw/

翻译：我们提出MedShapeNet，这是一个包含解剖形状（如骨骼、器官、血管）及3D手术器械模型的大型数据集。在深度学习时代之前，统计形状模型（SSM）在医学图像分析中的广泛应用证明了形状常用于描述医疗数据。然而，当前医学成像领域最先进的深度学习算法主要基于体素。相反，在计算机视觉中，形状（包括体素占据网格、网格、点云及隐式表面模型）是3D中更受青睐的数据表示形式，这从顶级视觉会议（如IEEE/CVF计算机视觉与模式识别大会CVPR）上大量与形状相关的论文发表，以及ShapeNet（约51,300个模型）和Princeton ModelNet（127,915个模型）在计算机视觉研究中日益增长的普及度可见一斑。MedShapeNet作为这些常用形状基准的替代方案而创建，旨在促进数据驱动视觉算法向医疗应用的转化，并拓展了适配最先进视觉算法以解决关键医疗问题的机会。此外，MedShapeNet中的大多数医疗形状直接根据真实患者的影像数据建模，因此能很好地补充现有由计算机辅助设计（CAD）模型构成的形状基准。目前，MedShapeNet包含超过10万个医疗形状，并以配对数据形式提供标注。它也是一个可供自由获取的3D模型资源库，适用于扩展现实（虚拟现实VR、增强现实AR、混合现实MR）及医疗3D打印。本白皮书详细阐述了MedShapeNet的构建动机、形状采集流程、使用案例以及在线形状搜索门户（https://medshapenet.ikim.nrw/）的使用方法。