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Semantic Segmentation Dataset for AI-Based Quantification of Clean Mucosa in Capsule Endoscopy.

Jeong-Woo Ju^1,2, Heechul Jung³, Yeoun Joo Lee^1,4

¹Biomedical Research Institute, Pusan National University Yangsan Hospital, Yangsan 50612, Korea.

Medicina (Kaunas, Lithuania)

|March 26, 2022

Summary

Artificial intelligence (AI) offers an objective method for evaluating bowel cleanliness from capsule endoscopy (CE) images. A new dataset and convolutional neural network (CNN) algorithm achieved high accuracy in assessing mucosal cleanliness.

Keywords:

capsule endoscopy deep learning semantic segmentation small bowel cleanliness visualization scale

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Area of Science:

Medical Imaging
Artificial Intelligence
Gastroenterology

Background:

Capsule endoscopy (CE) bowel cleanliness assessment is subjective.
Objective evaluation using AI is needed.
Developing a robust AI model requires a large, high-quality dataset.

Purpose of the Study:

To create a large-scale semantic segmentation dataset from CE images.
To train and validate a convolutional neural network (CNN) for objective bowel cleanliness assessment.
To develop and verify a formula for quantifying clean mucosal regions.

Main Methods:

Extracted and classified 10,033 CE frames into 2 or 3 classes (clean, dark, floats/bubbles).
Trained a CNN model using 169 videos and a semantic segmentation dataset.
Developed a visualization scale (VS) formula and evaluated its performance using mIoU and Dice index.

Main Results:

Achieved high performance metrics: 0.7716-0.8927 mIoU and 0.8627-0.9457 Dice index for 2- and 3-class segmentation.
Demonstrated high clean mucosal prediction accuracy: 94.4% (3-class) and 95.7% (2-class).
The developed VS formula showed performance nearly identical to ground truth.

Conclusions:

Established a comprehensive 10-stage semantic segmentation dataset from 179 patients.
Validated an AI-based approach for accurate ( >94%) and quantitative bowel cleanliness assessment.
The developed VS equation provides a reliable method for measuring clean mucosal regions in CE.