Nuclei panoptic segmentation and composition regression with multi-task deep neural networks

TL;DR

This paper presents a multi-task deep learning approach for nuclei segmentation, classification, and quantification in histology images, advancing computational pathology through a combined panoptic segmentation and regression framework.

Contribution

It introduces a novel multi-task neural network that performs both nuclei segmentation and composition regression for improved accuracy in histology image analysis.

Findings

Achieved competitive results in the CoNIC challenge

Demonstrated effective separation of neighboring nuclei

Enhanced nuclei quantification accuracy

Abstract

Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology. The Colon Nuclei Identification and Counting (CoNIC) Challenge is held to help drive forward research and innovation for automatic nuclei recognition in computational pathology. This report describes our proposed method submitted to the CoNIC challenge. Our method employs a multi-task learning framework, which performs a panoptic segmentation task and a regression task. For the panoptic segmentation task, we use encoder-decoder type deep neural networks predicting a direction map in addition to a segmentation map in order to separate neighboring nuclei into different instances