OrganSegC2F is a fast and effective organ segmentation approach which works on 3D medical data. It is built upon a 2D deep segmentation network, like FCN, and applies a coarse-to-fine idea to depecting the boundary of the organ more accurately. It produces the state-of-the-art accuracy on the NIH pancreas segmentation task, and also works well in our newly collected dataset which contains 18 abdominal organs.
UnrealCV is a data generation tool to extract information from photorealistic video games.
DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe. It combines (1) atrous convolution to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks, (2) atrous spatial pyramid pooling to robustly segment objects at multiple scales with filters at multiple sampling rates and effective fields-of-views, and (3) densely connected conditional random fields (CRF) as post processing.
We present a method for estimating articulated human pose from a single static image based on a graphical model with novel pairwise relations that make adaptive use of local image measurements. More precisely, we specify a graphical model for human pose which exploits the fact the local image measurements can be used both to detect parts (or joints) and also to predict the spatial relationships between them (Image Dependent Pairwise Relations).