mlsanityData to 7 Oct 2026

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

arXiv 1606.06650 ↗First posted 21 Jun 2016cs.CVcited by 1,471

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Cited by 72 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 1,471 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jun 2016.

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Citing papers per year: 2021: 245; 2022: 276; 2023: 266; 2024: 263; 2025: 235; 2026: 186.

1,471 papers.

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