Enhanced Nuclei Segmentation in Histopathological Images Using a Novel Preprocessing Pipeline and Deep Learning

In the evolving field of medical imaging analysis, the accurate segmentation of nuclei from histopathological images is a critical step toward enabling automated diagnostics and quantitative pathology. Traditional methods, while pioneering, often fall short when confronted with the challenges of overlapping, densely clustered nuclei and varying histological appearances across different organs. We address these challenges by introducing a novel approach that uses the advanced U-Net++ architecture, augmented with a combined Dice-Cross-Entropy (Dice-CE) loss function. Our model is distinguished by its training on a comprehensive dataset encompassing images from 31 different human and mouse organs. It represents a significant leap in diversity and complexity compared to previous studies on specific organ histopathology. Through meticulous techniques, including color normalization and advanced filtering, alongside the strategic implementation of the combined loss function, our method demonstrates superior segmentation performance. Notably, it outperforms existing models in delineating nuclei with high precision and accuracy, as evidenced by our experiments on the NuInsSeg dataset.