VoteNet

VoteNet is a deep-learning-based label fusion strategy for multi-atlas segmentation (MAS) which locally selects a set of reliable atlases whose labels are then fused via plurality voting. By selecting a good initial atlas set MAS with VoteNet significantly outperforms a number of other label fusion strategies as well as a direct deep-learning (DL) segmentation approach. VoteNet makes use of a fast DL registration approach.

The VoteNet repository, which contains the code for VoteNet, VoteNet+, and VoteNet++ can be found here: https://github.com/uncbiag/VoteNet-Family