High-accuracy instance segmentation of cellular structures in head and neck squamous cell carcinoma histopathology using a cyto R-CNN deep learning framework.

This study develops a deep learning-based model to automate the instance segmentation of nuclei and whole cells in hematoxylin and eosin-stained head and neck squamous cell carcinoma (HNSCC) images, aiming to enhance the efficiency and objectivity of pathological assessment. Using the CytoNuke dataset, which contains 83 images with 6598 annotated instances, the methodology involved image normalization, extensive data augmentation, and the development of a task-specific Cyto R-CNN framework optimized for the complex tissue architecture of HNSCC. By integrating a dual-branch decoder for joint nucleus-cell segmentation and a boundary-aware Contour Loss, the model explicitly addresses the challenges of cellular overlap and indistinct boundaries inherent in HNSCC histology. Model performance was optimized through cross-validation and hyperparameter tuning. The results demonstrate that the proposed model achieves a mean Dice coefficient exceeding 0.90 for both nuclei and whole cells, significantly outperforming the widely used semantic segmentation baseline (U-Net) and traditional image processing techniques. It enables the precise delineation of cellular structures and the generation of reliable quantitative metrics, thereby reducing the time required for analytical tasks. In conclusion, this study successfully establishes a precise and efficient deep learning model for automated instance segmentation in HNSCC histopathology, offering quantitative support to diagnostic practice. Future work will focus on expanding the dataset and evaluating the model across broader image collections and clinical applications.