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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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High-Resolution Swin Transformer for Automatic Medical Image Segmentation.

Chen Wei1, Shenghan Ren2, Kaitai Guo3

  • 1College of Economics and Management, Xi'an University of Posts & Telecommunications, Xi'an 710061, China.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary

This study introduces the high-resolution Swin Transformer network (HRSTNet) for medical image segmentation. HRSTNet maintains high spatial precision, achieving performance comparable to state-of-the-art methods.

Keywords:
Swin TransformerTransformermedical image segmentationself-attention

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Area of Science:

  • Medical Image Analysis
  • Deep Learning
  • Computer Vision

Background:

  • Accurate medical image segmentation relies on feature map resolution.
  • Existing Transformer-based U-Net architectures may lose spatial precision during high-resolution recovery.
  • High-resolution network (HRNet) design principles offer an alternative approach.

Purpose of the Study:

  • To develop a Transformer-based network that preserves spatial precision in medical image segmentation.
  • To leverage the HRNet design by integrating Transformer blocks for continuous multi-resolution feature exchange.

Main Methods:

  • Proposed the high-resolution Swin Transformer network (HRSTNet).
  • Replaced convolutional layers with Transformer blocks within an HRNet-style architecture.
  • Enabled continuous feature map information exchange across different resolutions.

Main Results:

  • HRSTNet demonstrated performance comparable to state-of-the-art Transformer-based U-Net-like architectures.
  • Evaluated on diverse datasets: 2021 Brain Tumor Segmentation, Medical Segmentation Decathlon (liver), and BTCV multi-organ segmentation.

Conclusions:

  • The HRSTNet effectively addresses the spatial precision limitations of traditional U-Net-like Transformer networks.
  • HRSTNet offers a promising alternative for high-accuracy medical image segmentation tasks.