Automated whole slide morphometry of sural nerve biopsy using machine learning

The morphometry of sural nerve biopsies, such as fibre diameter and myelin thickness, helps us understand the underlying mechanism of peripheral neuropathies. However, in current clinical practice, only a portion of the specimen is measured manually because of its labour-intensive nature. In this study, we aimed to develop a machine learning-based application that inputs a whole slide image (WSI) of the biopsied sural nerve and automatically performs morphometric analyses.

Our application consists of three supervised learning models: (1) nerve fascicle instance segmentation, (2) myelinated fibre detection and (3) myelin sheath segmentation. We fine-tuned these models using 86 toluidine blue-stained slides from various neuropathies and developed an open-source Python library.

Performance evaluation showed (1) a mask average precision (AP) of 0.861 for fascicle segmentation, (2) box AP of 0.711 for fibre detection and (3) a mean intersection over union (mIoU) of 0.817 for myelin segmentation. Our software identified 323,298 nerve fibres and 782 fascicles in 70 WSIs. Small and large fibre populations were objectively determined based on clustering analysis. The demyelination group had large fibres with thinner myelin sheaths and higher g-ratios than the vasculitis group. The slope of the regression line from the scatter plots of the diameters and g-ratios was higher in the demyelination group than in the vasculitis group.

We developed an application that performs whole slide morphometry of human biopsy samples. Our open-source software can be used by clinicians and pathologists without specific machine learning skills, which we expect will facilitate data-driven analysis of sural nerve biopsies for a more detailed understanding of these diseases.