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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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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.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|April 25, 2025

Summary

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.

Keywords:

Cardiac image segmentation Causal learning Cross-modal segmentation

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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.