Learning-enhanced 3D fiber orientation mapping in thick cardiac tissues.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Materials Science

Background:

• Fibrous proteins like elastin and collagen are vital for cardiovascular structural integrity.
• 2D fiber orientation mapping is established for thin tissues, but robust 3D analysis tools for thick samples (e.g., embryonic hearts) are lacking.
• 3D fiber orientation data is crucial for computational vascular modeling and characterizing tissue microstructure.

Purpose of the Study:

• To develop and evaluate machine learning (ML) and deep learning (DL) techniques for analyzing 3D cardiovascular fiber structures in thick tissue samples.
• To compare the performance of ML/DL methods against traditional Fourier transform and directional filter approaches.
• To assess the potential of these advanced techniques for improving spatial accuracy and reducing manual preprocessing in fiber orientation analysis.

Main Methods:

• Employed ML/DL models, including Support Vector Regression (SVR), Convolutional Neural Networks (CNN), and Residual Network-50 (ResNet50), trained on synthetic and real confocal imaging datasets.
• Utilized attention mechanisms, specifically channel attention with ResNet50, to enhance model performance.
• Evaluated models on a mixed dataset (1200 samples) and a biological dataset (400 porcine/bovine samples).

Main Results:

• Support Vector Regression (SVR) achieved the highest accuracy with a normalized mean absolute error (nMAE) of 5.0% on the mixed dataset and 13.0% on the biological dataset.
• Deep learning models, CNN and ResNet50, showed nMAE of 12.0% and 11.0% (mixed) and 23.0% and 22.0% (biological), respectively.
• Channel attention ResNet50 improved performance, achieving an nMAE of 5.8% (mixed) and 21.0% (biological), demonstrating the benefit of attention mechanisms.

Conclusions:

• ML and DL techniques show significant potential for accurate 3D cardiovascular fiber orientation detection in thick tissue samples.
• These methods offer improved spatial accuracy and reduced reliance on manual preprocessing compared to traditional techniques.
• The study enables more detailed cardiovascular microstructural assessment, crucial for computational modeling and tissue engineering.