Embedding Tasks Into the Latent Space: Cross-Space Consistency for Multi-Dimensional Analysis in Echocardiography

Area of Science:

• Cardiovascular imaging and analysis
• Artificial intelligence in medicine
• Biomedical signal processing

Background:

• Multi-dimensional echocardiography offers rich clinical insights but faces inter-task conflict challenges.
• Existing methods may overlook task hierarchies, leading to suboptimal constraints in complex analyses.
• Accurate quantification of cardiac indices and computer-aided diagnosis remain critical clinical needs.

Purpose of the Study:

• To introduce Cross-Space Consistency (CSC) for resolving inter-task conflict in multi-dimensional echocardiography.
• To enable unified analysis across pixel-level, image-level, and sequence-level tasks.
• To enhance the accuracy and robustness of cardiac function assessment.

Main Methods:

• Proposed Cross-Space Consistency (CSC) to embed multi-level tasks into a unified latent space.
• Reduced inherent task differences by aligning features in the common latent space.
• Constrained task weights to satisfy multiple conditions simultaneously.

Main Results:

• Achieved high performance in left ventricular (LV) segmentation (DSC = 0.932).
• Demonstrated accurate keypoint detection (MAE = 3.06mm).
• Showcased reliable keyframe identification (accuracy = 0.943) across five large-scale datasets.

Conclusions:

• CSC effectively addresses inter-task conflict in multi-dimensional echocardiography.
• The method provides robust and accurate multi-dimensional cardiac function analysis.
• CSC shows significant potential for computer-aided diagnosis in large clinical datasets.