3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2016

AUTHORS

Özgün Çiçek , Ahmed Abdulkadir , Soeren S. Lienkamp , Thomas Brox , Olaf Ronneberger

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. More... »

PAGES

424-432

Book

TITLE

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

ISBN

978-3-319-46722-1
978-3-319-46723-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-46723-8_49

DOI

http://dx.doi.org/10.1007/978-3-319-46723-8_49

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084908686


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