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Causal recurrent intervention for cross-modal cardiac image segmentation
Qixin Lin1, Saidi Guo2, Heye Zhang3
1School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China.
Insights
This study introduces the causal recurrent intervention (CRI) method to improve cross-modal cardiac image segmentation. CRI addresses confounding factors, enabling more accurate cardiac disease analysis from diverse imaging data.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiology
Background:
- Cross-modal cardiac image segmentation is crucial for diagnosing cardiovascular diseases.
- Manual annotation of cardiac images is labor-intensive, hindering clinical and deep learning applications.
- Existing methods struggle with cross-domain confounding from modality and view variations.
Purpose of the Study:
- To develop a novel method for accurate cross-modal cardiac image segmentation.
- To overcome challenges in cross-modal learning caused by domain confounding.
- To reduce the reliance on extensive manual annotations in cardiac imaging.
Main Methods:
- Proposes the causal recurrent intervention (CRI) method based on a structural causal model.
- Integrates image slices into a sequence to handle high-dimensional variations.
- Distinguishes and separates stable (modal, view) and dynamic factors for improved representation.
Main Results:
- The CRI method demonstrates promising and productive performance in cross-modal cardiac image segmentation.
- Experimental results on 1697 cardiac image examples validate the method's effectiveness.
- The approach successfully addresses confounding factors in cross-modal learning.
Conclusions:
- The causal recurrent intervention (CRI) method offers a robust solution for cross-modal cardiac image segmentation.
- This technique can enhance the precision of cardiac structure and function analysis.
- CRI facilitates leveraging multi-modal data more effectively for clinical research and diagnosis.
Abstract:
Cross-modal cardiac image segmentation is essential for cardiac disease analysis. In diagnosis, it enables clinicians to obtain more precise information about cardiac structure or function for potential signs by leveraging specific imaging modalities. For instance, cardiovascular pathologies such as myocardial infarction and congenital heart defects require precise cross-modal characterization to guide clinical decisions. The growing adoption of cross-modal segmentation in clinical research underscores its technical value, yet annotating cardiac images with multiple slices is time-consuming and labor-intensive, making it difficult to meet clinical and deep learning demands. To reduce the need for labels, cross-modal approaches could leverage general knowledge from multiple modalities. However, implementing a cross-modal method remains challenging due to cross-domain confounding. This challenge arises from the intricate effects of modality and view alterations between images, including inconsistent high-dimensional features. The confounding complicates the causality between the observation (image) and the prediction (label), thereby weakening the domain-invariant representation. Existing disentanglement methods face difficulties in addressing the confounding due to the insufficient depiction of the relationship between latent factors. This paper proposes the causal recurrent intervention (CRI) method to overcome the above challenge. It establishes a structural causal model that allows individual domains to maintain causal consistency through interventions. The CRI method integrates diverse high-dimensional variations into a singular causal relationship by embedding image slices into a sequence. This approach further distinguishes stable and dynamic factors from the sequence, subsequently separating the stable factor into modal and view factors and establishing causal connections between them. It then learns the dynamic factor and the view factor from the observation to obtain the label. Experimental results on cross-modal cardiac images of 1697 examples show that the CRI method delivers promising and productive cross-modal cardiac image segmentation performance.
