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Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm.

Minh-Trieu Tran1, Soo-Hyung Kim1, Hyung-Jeong Yang1

  • 1Department of Artificial Intelligence Convergence, Chonnam National University, 77 Yongbong-ro, Gwangju 500757, Korea.

Sensors (Basel, Switzerland)
|July 20, 2021
PubMed
Summary

This study presents an automated framework for segmenting the esophagus in CT scans, overcoming challenges like small size and low contrast. The method improves accuracy and efficiency in radiotherapy planning.

Keywords:
deep learningesophagus segmentationspatial attention module

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Computational Anatomy

Background:

  • Accurate organ segmentation in Computed Tomography (CT) is crucial for radiotherapy treatment planning.
  • The esophagus presents significant segmentation challenges due to its small size, ambiguous boundaries, and low contrast in CT images.
  • Existing methods often require substantial computational resources.

Purpose of the Study:

  • To develop a fully automated framework for esophagus segmentation from CT images.
  • To address the difficulties associated with segmenting the esophagus, improving accuracy and efficiency.
  • To provide a computationally efficient solution for esophagus segmentation.

Main Methods:

  • A novel framework processing slice images from 3D CT data, reducing computational load.
  • Integration of spatial attention mechanisms with atrous spatial pyramid pooling for effective esophagus localization.
  • Utilization of group normalization for stable performance independent of batch sizes.
  • Application of the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm for robust segmentation results.

Main Results:

  • The proposed method demonstrated improved Dice and Hausdorff Distance scores after applying the STAPLE algorithm.
  • Evaluation on SegTHOR and StructSeg 2019 datasets showed superior performance compared to state-of-the-art methods.
  • The framework achieved promising results in challenging esophagus segmentation tasks.

Conclusions:

  • The developed automated framework offers an effective solution for esophagus segmentation in CT images.
  • The method's efficiency and accuracy contribute to improved radiotherapy treatment planning.
  • This approach represents a significant advancement in challenging medical image analysis tasks.