SAMS-NET: Stain-aware multi-scale network for instance-based nuclei segmentation in histology images

Abstract

Segmentation of nuclear material in histology slides is an important step in the digital pathology work-flow, due to the ability for nuclei to act as key diagnostic markers. Manual segmentation can be a laborious task, where pathologists are often required to analyse many nuclei within a whole slide image (WSI). The rise in digital pathology has been matched with an increase in interest for automated nuclei segmentation in Hematoxylin & Eosin (H&E) stained histology images, yet this remains a challenge due to the heterogeneous appearance of different types of nuclei. This heterogeneity can lead to nuclei having a variable Hematoxylin intensity, which often has detrimental effects on the success of current methods. We propose a deep multi-scale neural network, with a novel loss function that is sensitive to the Hematoxylin intensity, for precise object-level nuclei segmentation. We show that the proposed network outperforms all competing methods for the computational precision medicine (CPM) nuclei segmentation challenge dataset as part of MICCAI 2017.