3D U Net: Learning Dense Volumetric Segmentation from Sparse Annotation
This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi automated setup, the user annotates some slices in the volume to be segmented. The network learns from these sparse annotations and provides a dense 3D segmentation. (2) In a fully automated setup, we assume that a representative, sparsely annotated training set exists. Trained on this data set, the network densely segments new volumetric images. The proposed network extends the previous u net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts. The implementation performs on the fly elastic deformations for efficient data augmentation during training. It is trained end to end from scratch, i.e. no pre trained network is required. We test the performance of the proposed method on a complex, highly variable 3D structure, the Xenopus kidney, and achieve good results for both use cases.
MICCAI