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Leverage prior texture information in deep learning-based liver tumor segmentation: A plug-and-play Texture-Based

Zhaoshuo Diao1, Huiyan Jiang2, Yang Zhou1

  • 1Software College, Northeastern University, Shenyang 110819, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|March 23, 2023
PubMed
Summary

Accurately segmenting liver tumors in CT scans is challenging due to varied textures. A new Texture-based Auto Pseudo Label (TAPL) module enhances segmentation, particularly for small tumors, by learning texture differences.

Keywords:
Auto multiple pseudo-labelCTLiver tumorSegmentationTexture

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiotherapy

Background:

  • Accurate liver and tumor segmentation from CT scans is vital for clinical treatment and radiotherapy.
  • Current U-Net based methods struggle with diverse liver tumor shapes and textures, often treating them as a single class.
  • Texture information is critical for distinguishing various liver tumor types.

Purpose of the Study:

  • To improve liver and tumor segmentation accuracy, especially for small and varied tumors.
  • To introduce a novel module that leverages texture information for enhanced segmentation.
  • To enable neural networks to learn texture differences between diverse liver tumor classes.

Main Methods:

  • Proposed a plug-and-play Texture-based Auto Pseudo Label (TAPL) module.
  • TAPL module includes texture enhancement and a texture-based pseudo label generator.
  • Enhanced CT image regions with significant texture variations and classified tumors based on texture.

Main Results:

  • The TAPL module improved segmentation accuracy, particularly for small tumors.
  • Experimental results on clinical and Lits2017 datasets demonstrated superior performance compared to single-class segmentation methods.
  • The method effectively utilizes texture information to differentiate tumor types.

Conclusions:

  • The proposed TAPL module offers a significant advancement in liver tumor segmentation.
  • This approach is more effective for segmenting diverse and small liver tumors.
  • Leveraging texture information enhances the precision of medical image segmentation for improved patient care.