Automated Segmentation of Hepatic Vessels and Lobules in Whole-Slide Images Using U-Net Models

The repository contains curated datasets and trained models developed for the automated analysis of liver histopathology whole slide images (WSIs), specifically focused on the detection and segmentation of liver lobules and vascular structures. The dataset includes two ZIP files containing patch-level training and testing data for each task. Each dataset is organized into training/ and testing/ folders, with corresponding images/ and masks/ subdirectories. The masks provide pixel-level annotations for supervised segmentation. Additionally, the wholeslide image/ folders contain original WSIs and associated annotation files used for generating training samples. A separate Zip file archive includes the PyTorch .pth model files trained on these datasets, enabling reproducibility and further experimentation. An additional ZIP file titled TestSlides.zip provides independent whole slide liver images used to qualitatively evaluate model performance on unseen data.