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.

Fig. 1.

Cartoon representation of a thick 3D cardiovascular tissue of the whole heart. (b,c) The pre-processed visualization along the xz and xy-axis using raw data. (d) 3D rendering highlighting the spatial organization of collagen fibers.

Fig. 2.

Process of estimating fiber orientation using both synthetic and biological datasets. Synthetic images are generated through diffusion models, creating diverse fiber patterns, which are then compared with actual biological fiber images captured via microscopy. Feature extraction is performed on both datasets using HOG, emphasizing the directionality and distribution of fiber orientations. These features are subsequently input into a SVR model, where the input layer consists of feature vectors processed through kernel functions in the hidden layer. The output layer sums these contributions to predict fiber orientation. The analysis culminates in detailed orientation maps, exemplified by yellow arrows on a sample image, providing comprehensive insights into the structural organization of fibers in synthetic and biological samples.

Fig. 3.

FFT Analysis for 2D Synthetic Data. (a-d) Lines with 45, 60 and 135 degree angles are produced as binary scale using MATLAB, and the graph of the analysis results is shown using 2D FFT and x axes represent to angle of line, y axis represent to frequency spectrum.

Fig. 4.

Comparison of fiber orientation estimation in different tissue samples using FFT and SVR. The top row (a-c) shows fiber orientation maps (red arrows) overlaid on green fluorescence images for clinically approved bovine (a), clinically approved porcine (b), and biological porcine (c) tissues analyzed using FFT. The corresponding histograms (d-f) depict the distribution of fiber angles obtained from FFT analysis, with angles ranging from 20 to 160 degrees. The bottom row (g-i) displays fiber orientation maps (red arrows) for the same tissue samples analyzed using SVR. The associated histograms (j-l) show the distribution of fiber angles derived from SVR analysis, with angles concentrated around 100 degrees. This comparison highlights the differences in fiber orientation results between FFT and SVR methods across different tissue types.

Fig. 5.

Analysis of Fiber Orientation via 2D FFT for clinically approved bovine, clinically approved porcine, and biological porcine samples. Eight samples were analyzed for each case, illustratingthemeanfiberorientationangles,accompaniedbystandarddeviationbars. These eight samples consist of four samples from four different pericardia, with two different locations each, providing comprehensive insights into fiber orientation variability across different anatomical sources and locations

Fig. 6.

3D Synthetic Torus Analysis Using 3D FFT. (a, b) Combined visualization showing the 3D structure of the helix and torus with detailed fiber orientation analysis using 3D FFT.

Fig. 7.

Visual representation and angle distribution of collagen fiber orientation. (a) and (d) show the collagen fiber structures obtained from the diffusion model. (b) and (e) are magnified views of the yellow highlighted areas in (a) and (d), respectively, providing a detailed view of fiber orientation within those regions. (c) and (f) are angle distribution histograms corresponding to the fiber structures shown in (a) and (d), respectively.

Fig. 8.

3D visualization and orientation analysis of fiber structures in clinically approved bovine, clinically approved porcine, and biological porcine tissues. a-c show 3D reconstructions of confocal microscopy slices, processed and visualized using Huygens, for clinically approved bovine (a), clinically approved porcine (b), and biological porcine (c) samples. d-f display the angular distribution of fiber orientations along the xy-axis (blue) and xz-axis (red), obtained through Fourier transform analysis in MATLAB. g-i provide the angular distribution of fiber orientations obtained from Support Vector Regression.

Fig. 9.

Comprehensive analysis of 3D collagen fiber orientation in chick embryo heart samples using advanced imaging and modeling techniques. The first row focuses on the right ventricle (RV), showing a 3D rendering of the heart (first column), a high-resolution 2D confocal microscopy image of collagen fibers (second column), a 3D vector field visualization of fiber orientations (third column), and a polar distribution plot illustrating predominant fiber orientation around 90 degrees (fourth column). The second row provides similar analyses for the left ventricle (LV), with corresponding visualizations and orientation data.