- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

The Medical Segmentation Decathlon.

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution.

TAGNet: Learning Configurable Context Pathways for Semantic Segmentation.

State-of-the-art semantic segmentation methods capture the relationship between pixels to facilitate contextual information exchange. Advanced methods utilize fixed pathways for context exchange, lacking the flexibility to harness the most relevant context for each pixel. In this paper, we present Configurable Context Pathways (CCPs), a novel model for establishing pathways for augmenting contextual information.

SCN: Switchable Context Network for Semantic Segmentation of RGB-D Images.

Context representations have been widely used to profit semantic image segmentation. The emergence of depth data provides additional information to construct more discriminating context representations. Depth data preserves the geometric relationship of objects in a scene, which is generally hard to be inferred from RGB images.