Eosinophils Instance Object Segmentation on Whole Slide Imaging Using
  Multi-label Circle Representation
Yilin Liu Ruining Deng Juming Xiong Regina N Tyree Hernan Correa Girish Hiremath Yaohong Wang Yuankai Huo
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.