3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
1University of Freiburg 2BIOSS 3University Hospital Freiburg 4University Medical Center Freiburg 5Google DeepMind
Abstract
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.
References
Ö, Çiçek et al., “3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation“, Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2016. [BibTex]