Neural network auto-segmentation of serial-block-face scanning electron microscopy images exhibit collagen fibril structural differences with tendon type and health.

Abstract

A U-Net machine learning algorithm was adapted to automatically segment tendon collagen fibril cross-sections from serial block face scanning electron microscopy (SBF-SEM) and create three-dimensional (3D) renderings. We compared the performance of routine Otsu thresholding and U-Net for a positional tendon that has low fibril density (rat tail tendon), an energy-storing tendon that has high fibril density (rat plantaris tendon), and a high fibril density tendon hypothesized to have disorganized 3D ultrastructure (degenerated rat plantaris tendon). The area segmentation of the tail and healthy plantaris tendon had excellent accuracy for both the Otsu and U-Net, with an Intersection over Union (IoU) of 0.8. With degeneration, only the U-Net could accurately segment the area, whereas Otsu IoU was only 0.45. For boundary validation, the U-Net outperformed Otsu segmentation for all tendons. The fibril diameter from U-Net was within 10% of the manual segmentation, however, the Otsu underestimated the fibril diameter by 39% in healthy plantaris and by 84% in the degenerated plantaris. Fibril geometry was averaged across the entire image stack and compared across tendon types. The tail had a lower fibril area fraction (58%) and larger fibril diameter (0.31 µm) than the healthy plantaris (67% and 0.21 µm) and degenerated plantaris tendon (66% and 0.19 µm). This method can be applied to a large variety of tissues to quantify 3D collagen fibril structure.