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Source-free domain transfer algorithm with reduced style sensitivity for medical image segmentation.

Jian Lin1, Xiaomin Yu2, Zhengxian Wang2

  • 1Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital, Chengdu, China.

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|December 27, 2024
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Summary
This summary is machine-generated.

This study introduces a new source-free domain transfer algorithm (SFDT-RSS) for medical image segmentation. It significantly improves accuracy by reducing style sensitivity and enhancing generalization without needing source data.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Machine Learning

Background:

  • Unsupervised transfer learning for medical image segmentation faces challenges with error propagation due to inaccessible source domain data.
  • Existing algorithms struggle to generalize across different medical imaging styles and datasets without direct access to the original training data.

Purpose of the Study:

  • To develop a novel source-free domain transfer algorithm (SFDT-RSS) that minimizes style sensitivity and enhances generalization for medical image segmentation.
  • To adapt pre-trained models to new target domains without requiring access to the source domain data, addressing a key limitation in current methods.

Main Methods:

  • SFDT-RSS employs a generalization strategy for initial source domain model pre-training.
  • It utilizes an interpatch style transfer (ISS) strategy with a Transformer architecture for self-training to reduce style sensitivity and improve generalization.
  • A model-agnostic adaptive confidence regulation (ACR) loss is used during the domain transfer phase to fine-tune the source model.

Main Results:

  • The proposed SFDT-RSS algorithm demonstrated significant improvements in segmentation accuracy across five public datasets for unsupervised cross-domain organ segmentation.
  • Specific accuracy improvements ranged from 2.64% to 3.32% compared to existing unsupervised domain transfer algorithms.
  • The method effectively enhances generalization capability and reduces reliance on specific image styles.

Conclusions:

  • SFDT-RSS offers a robust solution for unsupervised cross-domain medical image segmentation by effectively addressing style sensitivity and data accessibility issues.
  • The algorithm's performance highlights the potential of source-free domain transfer methods in medical imaging applications.
  • The combination of ISS and ACR loss contributes to improved segmentation accuracy and model generalizability.