mlsanityData to 7 Oct 2026

Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images

arXiv 2205.15941 ↗First posted 31 May 2022eess.IV cs.CVcited by 1

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Abstract (arXiv)

In recent years, 3D convolutional neural networks have become the dominant approach for volumetric medical image segmentation. However, compared to their 2D counterparts, 3D networks introduce substantially more training parameters and higher requirement for the GPU memory. This has become a major limiting factor for designing and training 3D networks for high-resolution volumetric images. In this work, we propose a novel memory-efficient network architecture for 3D high-resolution image segmentation. The network incorporates both global and local features via a two-stage U-net-based cascaded framework and at the first stage, a memory-efficient U-net (meU-net) is developed. The features learnt at the two stages are connected via post-concatenation, which further improves the information flow. The proposed segmentation method is evaluated on an ultra high-resolution microCT dataset with typically 250 million voxels per volume. Experiments show that it outperforms state-of-the-art 3D segmentation methods in terms of both segmentation accuracy and memory efficiency.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 24 references that are in the map. First posted on arXiv 31 May 2022.

Builds on

19 of 24 references.

References · 19

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7 more
  1. 2021Multi-Compound Transformer for Accurate Biomedical Image Segmentationcited by 42
  2. 2017Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNetscited by 32
  3. 2017Deeply-Supervised CNN for Prostate Segmentationcited by 30
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentationcited by 1