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DCL: Dynamic Causal Learning for Cross-Modality Cardiac Image Segmentation
Insights
Dynamic Causal Learning (DCL) effectively addresses spatial-temporal confounding in cross-modality cardiac image segmentation. This novel method improves knowledge transfer between different imaging types, enhancing diagnostic accuracy for heart disease.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiology
Background:
- Accurate cardiac image segmentation is crucial for diagnosing and treating heart disease.
- Existing methods struggle with spatial-temporal confounding, hindering cross-modality knowledge transfer due to intertwined anatomy and modality features.
Purpose of the Study:
- To propose a novel Dynamic Causal Learning (DCL) method to overcome spatial-temporal confounding in cross-modality cardiac image segmentation.
- To improve the transferability of learned features across different cardiac imaging modalities (MR, CT, US).
Main Methods:
- Developed Dynamic Causal Learning (DCL) using multi-dimensional causal intervention to address spatial-temporal confounding.
- Integrated historical optimal interventions for knowledge transfer across temporal contexts.
- Employed a diffusion mechanism to ensure causal invariance of anatomical features.
Main Results:
- The DCL method achieved a mean Dice score of 0.951 on cross-modality cardiac images.
- DCL significantly outperformed existing advanced segmentation methods.
- Demonstrated effectiveness across multiple imaging modalities including MR, CT, and US.
Conclusions:
- Dynamic Causal Learning (DCL) effectively solves spatial-temporal confounding in cross-modality cardiac image segmentation.
- The proposed method enhances model performance and knowledge transfer across diverse cardiac imaging modalities.
- DCL offers a promising solution for improving cardiac image analysis and clinical decision-making.
Abstract:
Accurate cross-modality cardiac image segmentation is essential for effectively diagnosing and treating heart disease. Different imaging modalities help to determine suitable pre-procedure planning. However, most methods face the difficulty of spatial-temporal confounding, where the anatomy element and modality element of cardiac images are intertwined across both spatial and temporal dimensions. It is derived from the imaging diversity and structure diversity of cardiac images. The spatial-temporal confounding hinders knowledge transfer between cardiac images on different modalities. In this paper, we propose a novel dynamic causal learning (DCL) to solve spatial-temporal confounding. The DCL explores multi-dimensional causal intervention to consider not only the causal relationship between images and labels, but also the causality in time dimension and space dimension. It integrates historical optimal interventions and facilitates the transfer of this knowledge across temporal contexts. In addition, the DCL utilizes the diffusion mechanism to further ensure that the extracted anatomy element remains causal invariant, improving model performance across multiple imaging modalities. Extensive experiments on cross-modality cardiac images (MR, CT, and US) demonstrate the effectiveness of the DCL (mean Dice = 0.951), outperforming other advanced segmentation methods. DCL is freely accessible at https://github.com/asdww0721ww/DCL.
