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¹Department of Computer Science, Vanderbilt University, Nashville, TN, USA.

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Summary

This summary is machine-generated.

A new multi-label CircleSnake model enhances eosinophil segmentation for diagnosing eosinophilic esophagitis (EoE). This automated method improves accuracy and efficiency in identifying esophageal inflammation, aiding clinical assessment.

Keywords:

CircleSnake Eosinophilic Esophagitis Mask R-CNN

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Area of Science:

Medical diagnostics
Computational pathology
Image analysis

Background:

Eosinophilic esophagitis (EoE) is a chronic inflammatory condition of the esophagus.
EoE diagnosis relies on counting esophageal eosinophils (Eos), a labor-intensive manual process.
Current methods lack efficiency and can be subjective, necessitating automated solutions.

Purpose of the Study:

To develop an automated method for accurate eosinophil instance segmentation in EoE diagnosis.
To adapt the CircleSnake model for multi-label segmentation of eosinophils.
To compare the performance of the proposed model against existing methods like Mask R-CNN.

Main Methods:

Proposed a multi-label CircleSnake model, extending the original single-label CircleSnake.
Applied the model to instance segmentation of eosinophils (Eos) in esophageal tissue images.
Evaluated performance using average precision (AP) and compared with Mask R-CNN and DeepSnake.

Main Results:

The multi-label CircleSnake model demonstrated superior performance in identifying and segmenting eosinophils.
Achieved higher average precision (AP) compared to Mask R-CNN and DeepSnake models.
The automated approach streamlines the assessment process for EoE.

Conclusions:

The multi-label CircleSnake model offers a promising automated solution for eosinophil segmentation in EoE.
This advancement can improve diagnostic accuracy and efficiency in clinical practice.
Publicly available source code facilitates further research and application.