Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation

TL;DR

This paper introduces a multi-label CircleSnake model for automatic eosinophil segmentation in whole slide images, improving accuracy over existing methods and aiding EoE diagnosis.

Contribution

It extends the CircleSnake model to handle multi-label segmentation, enabling precise identification of eosinophils in histopathology images.

Findings

CircleSnake outperforms Mask R-CNN and DeepSnake in average precision

The multi-label model effectively segments multiple eosinophil types

Automated segmentation improves diagnostic efficiency for EoE

Abstract

Eosinophilic esophagitis (EoE) is a chronic and relapsing disease characterized by esophageal inflammation. Symptoms of EoE include difficulty swallowing, food impaction, and chest pain which significantly impact the quality of life, resulting in nutritional impairments, social limitations, and psychological distress. The diagnosis of EoE is typically performed with a threshold (15 to 20) of eosinophils (Eos) per high-power field (HPF). Since the current counting process of Eos is a resource-intensive process for human pathologists, automatic methods are desired. Circle representation has been shown as a more precise, yet less complicated, representation for automatic instance cell segmentation such as CircleSnake approach. However, the CircleSnake was designed as a single-label model, which is not able to deal with multi-label scenarios. In this paper, we propose the multi-label CircleSnake model for instance segmentation on Eos. It extends the original CircleSnake model from a single-label design to a multi-label model, allowing segmentation of multiple object types. Experimental results illustrate the CircleSnake model's superiority over the traditional Mask R-CNN model and DeepSnake model in terms of average precision (AP) in identifying and segmenting eosinophils, thereby enabling enhanced characterization of EoE. This automated approach holds promise for streamlining the assessment process and improving diagnostic accuracy in EoE analysis. The source code has been made publicly available at https://github.com/yilinliu610730/EoE.