Cellular Segmentation and Composition in Routine Histology Images using Deep Learning
This addresses the need for automated analysis of nuclei in pathology images to aid cancer prognosis, though it appears incremental as it applies existing methods to a new dataset.
The paper tackles automated nuclei segmentation and classification in colorectal cancer histology images, achieving a PQ of 0.58 for segmentation with HoVer-Net and an overall R² score of 0.53 for cellular composition prediction with ALBRT.
Identification and quantification of nuclei in colorectal cancer haematoxylin \& eosin (H\&E) stained histology images is crucial to prognosis and patient management. In computational pathology these tasks are referred to as nuclear segmentation, classification and composition and are used to extract meaningful interpretable cytological and architectural features for downstream analysis. The CoNIC challenge poses the task of automated nuclei segmentation, classification and composition into six different types of nuclei from the largest publicly known nuclei dataset - Lizard. In this regard, we have developed pipelines for the prediction of nuclei segmentation using HoVer-Net and ALBRT for cellular composition. On testing on the preliminary test set, HoVer-Net achieved a PQ of 0.58, a PQ+ of 0.58 and finally a mPQ+ of 0.35. For the prediction of cellular composition with ALBRT on the preliminary test set, we achieved an overall $R^2$ score of 0.53, consisting of 0.84 for lymphocytes, 0.70 for epithelial cells, 0.70 for plasma and .060 for eosinophils.