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Interactive Echocardiography Translation Using Few-Shot GAN Transfer Learning.

Long Teng1, ZhongLiang Fu1, Qian Ma2

  • 1Chengdu Institute of Computer Application, University of Chinese Academy of Sciences, Beijing, China.

Computational and Mathematical Methods in Medicine
|April 8, 2020
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Summary
This summary is machine-generated.

This study introduces a novel two-step transfer learning method for interactive echocardiography translation, enabling pixel-level accuracy with limited data. The approach effectively bridges the gap between ultrasound images and anatomical sketches, enhancing medical education.

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Area of Science:

  • Medical imaging and visualization
  • Deep learning in healthcare
  • Biomedical engineering

Background:

  • Interactive echocardiography translation aids cardiac anatomy mastery through pixel-level image correlation.
  • Existing methods split image segmentation and synthesis, hindering pixel-level translation and deep learning application due to limited annotations.
  • Challenges persist in leveraging deep learning for echocardiography translation with scarce annotated data.

Purpose of the Study:

  • To develop an effective interactive translation method for echocardiography using limited annotations.
  • To enable pixel-level correspondence between ultrasound images and sketch representations.
  • To accelerate the development of new applications in biomedical computer-aided image translation.

Main Methods:

  • A two-step transfer learning approach was proposed, involving pre-training independent ultrasound-to-sketch (U2S) and sketch-to-ultrasound (S2U) parent networks.
  • The U2S parent network utilized U-Net on a public segmentation dataset (VOC2012), while the S2U parent network employed decoder networks.
  • An encoder was attached to the S2U network, and both U2S and S2U were jointly transferred within a Conditional Generative Adversarial Network (CGAN) framework for few-shot learning.

Main Results:

  • Quantitative and qualitative analyses demonstrated the effectiveness of the proposed algorithm across 1-shot, 5-shot, and 10-shot transfer learning scenarios.
  • The method successfully achieved interactive translation, proving the efficacy of few-shot transfer learning.
  • The approach significantly accelerates the development of new applications compared to building models from scratch.

Conclusions:

  • The proposed few-shot transfer learning approach enables effective interactive echocardiography translation with minimal annotations.
  • This method overcomes previous limitations in achieving pixel-level correspondence and facilitates deep learning applications in data-scarce environments.
  • The approach holds significant potential for advancing biomedical computer-aided image translation, particularly where annotated data is precious.