Automated segmentation of hepatic vessels and lobules in whole-slide images using U-net models.

Automated analysis of hepatic vascular structures and lobules within whole-slide histological images is critical for ensuring accurate and timely morphometric evaluations and facilitating advancements in computational liver histology. Nonetheless, the intricate morphology of the tissue, variability in staining techniques, and the requirements for standard high-resolution images present substantial challenges to the precision of segmentation processes. We present a robust deep-learning pipeline using adaptive patch extraction and specialized nnU-Net architectures for segmenting vessels, bile ducts, and lobules in Glutamine Synthetase and Picro-Sirius-Red stained porcine liver sections. Our architecture incorporates a weight-boosted nnU-Net framework with an adaptive, performance-based weight adjustment mechanism to effectively manage class imbalances and improve the detection of smaller vascular structures. The model was trained on four annotated whole-slide images and validated through comprehensive testing on eight additional independent slides. Geometric and intensity-based data transformations enhanced the robustness and generalizability of the segmentation models. Evaluations conducted through five-fold cross-validation, as well as assessments utilizing independent test datasets, resulted in Dice similarity scores: 0.968 for lobules, 0.795 for central veins, 0.895 for hepatic arteries, 0.665 for portal veins, and 0.694 for bile ducts. The developed segmentation pipeline additionally supports comprehensive morphometric analyses of structural parameters, including number and size (diameter, area) of vascular structures, bile ducts, and lobules; for example, the diameter of hepatic arteries ranges between 20-90 µm. These findings underscore the practical relevance of adaptable segmentation frameworks in advancing computational histological analysis of liver tissue.