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Related Concept Videos

Barrett Esophagus-I: Introduction01:21

Barrett Esophagus-I: Introduction

98
Barrett's esophagus is a medical condition where the esophageal mucosa is significantly damaged by stomach acid or other digestive fluids, often due to long-term exposure associated with gastroesophageal reflux disease (GERD). In GERD, a weakened or abnormally relaxed lower esophageal sphincter allows stomach acid to flow persistently into the esophagus.
This constant acid exposure transforms the esophagus's pink mucosal lining (stratified squamous epithelium) into a type of lining more...
98
Barrett Esophagus-II: Clinical Manifestations and Management01:21

Barrett Esophagus-II: Clinical Manifestations and Management

154
Individuals with Barrett's esophagus are often asymptomatic, but they may experience symptoms commonly associated with GERD, such as heartburn and acid regurgitation. Additional symptoms can include difficulty swallowing, chest pain, unintentional weight loss, blood in the stool (which may appear black, tarry, or bloody), and episodes of vomiting.
To diagnose Barrett's esophagus, healthcare providers often recommend an endoscopy for those showing symptoms of acid reflux. The procedure...
154

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Advancing Barrett's Esophagus Segmentation: A Deep-Learning Ensemble Approach with Data Augmentation and Model

Jiann-Der Lee1,2,3, Chih Mao Tsai1

  • 1Department of Electrical Engineering, Chang Gung University, Taoyuan 33302, Taiwan.

Bioengineering (Basel, Switzerland)
|January 22, 2024
PubMed
Summary

This study enhances Barrett's esophagus segmentation using deep learning. DenseNet backbones and ensemble methods significantly improved accuracy for precise medical image analysis.

Keywords:
Barrett’s esophagusDeeplabv3+U-Netdata augmentationdeep learningensemblemedical segmentation

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Barrett's esophagus requires accurate segmentation for diagnosis and treatment.
  • Deep learning models, particularly U-Net variants, show promise for medical image segmentation.

Purpose of the Study:

  • To investigate and optimize deep learning-based segmentation of Barrett's esophagus.
  • To evaluate the impact of different U-Net backbone architectures and ensemble strategies on segmentation performance.

Main Methods:

  • Exploration of U-Net models with various backbone architectures (e.g., DenseNet).
  • Application of rigorous data augmentation and ensemble techniques.
  • Fine-tuning ensemble weights using grid search and comparison with Deeplabv3+.

Main Results:

  • DenseNet backbones demonstrated superior performance in segmentation accuracy.
  • Tailored data augmentation and training U-Net models from scratch proved effective.
  • Ensemble methods consistently enhanced segmentation, achieving a mean intersection over union (IoU) score of approximately 0.94.

Conclusions:

  • Deep learning, especially U-Net with DenseNet backbones and ensemble strategies, offers a robust approach for Barrett's esophagus segmentation.
  • Optimized data augmentation and ensemble techniques are crucial for achieving high segmentation accuracy.
  • This research advances automated analysis in gastrointestinal diagnostics.