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MDA-Net: Multi-Dimensional Attention-Based Neural Network for 3D Image Segmentation

arXiv 2105.04508 ↗First posted 10 May 2021cs.CV eess.IVcited by 0

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Abstract (arXiv)

Segmenting an entire 3D image often has high computational complexity and requires large memory consumption; by contrast, performing volumetric segmentation in a slice-by-slice manner is efficient but does not fully leverage the 3D data. To address this challenge, we propose a multi-dimensional attention network (MDA-Net) to efficiently integrate slice-wise, spatial, and channel-wise attention into a U-Net based network, which results in high segmentation accuracy with a low computational cost. We evaluate our model on the MICCAI iSeg and IBSR datasets, and the experimental results demonstrate consistent improvements over existing methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 14 references that are in the map. First posted on arXiv 10 May 2021.

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10 of 14 references.

References · 10

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  3. 2017Squeeze-and-Excitation Networkscited by 4,789
  4. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  5. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  6. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  7. 2018Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networkscited by 122
  8. 2020Volumetric Attention for 3D Medical Image Segmentation and Detectioncited by 29
  9. 2019A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentationcited by 16
  10. 2019Hybrid Cascaded Neural Network for Liver Lesion Segmentationcited by 6

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