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Author Spotlight: Advancements in Intracardiac Echocardiography for Atrial Anatomy Assessment
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Adaptive dynamic inference for few-shot left atrium segmentation.

Jun Chen1, Xuejiao Li2, Heye Zhang2

  • 1School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, PR China; School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, Guangdong 518107, PR China.

Medical Image Analysis
|August 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces ADINet, a novel few-shot learning approach for accurate left atrium segmentation in cardiac MRI. ADINet effectively handles low-contrast images, improving atrial fibrillation treatment planning.

Keywords:
Atrial fibrillationFew-shot learningLA segmentationLCE CMR

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiology

Background:

  • Accurate left atrium (LA) segmentation in late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images is vital for atrial fibrillation treatment.
  • Few-shot learning offers a promising solution for LA segmentation with reduced reliance on extensive labeled data and improved cross-center generalization.
  • Low contrast between the LA and surrounding tissues in LGE CMR poses a significant challenge for few-shot segmentation.

Purpose of the Study:

  • To develop an Adaptive Dynamic Inference Network (ADINet) for accurate few-shot segmentation of the left atrium in LGE CMR images.
  • To explicitly model and leverage the differences between foreground (LA) and background regions.
  • To enhance the adaptability and generalization capabilities of few-shot learning models in medical image segmentation.

Main Methods:

  • ADINet utilizes dynamic collaborative inference (DCI) and dynamic reverse inference (DRI) to adapt convolution weights and indication information based on foreground and background knowledge.
  • Pixel-wise correlations are employed to adaptively allocate semantic-aware and spatial-specific parameters for different regions of query images.
  • Hierarchical supervision, including pixel-wise semantic and correlation supervision, is proposed to enforce spatial consistency and highlight foreground-background differences.

Main Results:

  • ADINet demonstrated superior segmentation performance compared to state-of-the-art methods on three LGE CMR datasets from different centers, using only ten samples.
  • The network effectively encoded differences between foreground and background regions by adapting convolution weights to spatial positions.
  • Indication information adaptively decoded foreground LA regions by leveraging spatial complementarity with background patterns.

Conclusions:

  • ADINet offers a robust and effective solution for few-shot left atrium segmentation in LGE CMR, addressing the challenge of low-intensity contrast.
  • The proposed adaptive dynamic inference mechanisms and hierarchical supervision contribute to improved segmentation accuracy and generalization.
  • This approach holds significant potential for advancing AI-driven diagnostic tools in cardiology, particularly for atrial fibrillation management.