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

Abstract

Fibrous proteins, such as elastin and collagen, are crucial for the structural integrity of the cardiovascular system. For thin tissue-engineered heart valves and surgical patches, the two-dimensional mapping of fiber orientation is well-established. However, for three-dimensional (3D) thick tissue samples, e.g., the embryonic whole heart, robust 3D fiber analysis tools are not available. This information is essential for computational vascular modeling and tissue microstructure characterization. Therefore, this study employs machine learning (ML) and deep learning (DL) techniques to analyze the 3D cardiovascular fiber structures in thick samples of porcine pericardium and embryonic whole hearts. It is hypothesized that ML/DL-based fiber orientation analysis will outperform traditional Fourier transform and directional filter methods by offering higher spatial accuracy and reduced dependency on manual preprocessing. We trained our ML/DL models on both synthetic and real-world cardiovascular datasets obtained from confocal imaging. The evaluation used a mixed dataset of 1200 samples and a porcine/bovine dataset of 400 samples. Support vector regression (SVR) demonstrated the highest accuracy, achieving a normalized mean absolute error (nMAE) of 5.0% on the mixed dataset and 13.0% on the biological dataset. Among DL models, convolutional neural network (CNN) and residual network-50 (ResNet50) had an nMAE of 12.0% and 11.0% on the mixed dataset and 23.0% and 22.0% on the biological dataset, respectively. Attention mechanisms improved performance further, with the channel attention ResNet50 achieving an nMAE of 5.8% on the mixed dataset and 21.0% on the biological dataset. These findings highlight the potential of ML and DL techniques in improving 3D fiber orientation detection, enabling detailed cardiovascular microstructural assessment.