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Fully automated esophagus segmentation with a hierarchical deep learning approach
Roger Trullo1,2, Caroline Petitjean1, Dong Nie2
1Normandie Univ, UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, 76000 Rouen, France.
Conference Proceedings. IEEE International Conference on Signal and Image Processing Applications
|October 23, 2018
Summary
Accurate esophagus segmentation in CT scans is crucial for radiotherapy. This study introduces a novel Fully Convolutional Network (FCN) architecture to improve low-contrast esophagus localization, achieving competitive results.
Area of Science:
- Medical Imaging
- Radiotherapy
- Computer Vision
Background:
- Accurate segmentation of organs at risk is essential for radiotherapy planning.
- Esophagus segmentation in CT images presents challenges due to low contrast walls.
Purpose of the Study:
- To improve esophagus segmentation accuracy in CT volumes.
- To develop an enhanced Fully Convolutional Network (FCN) architecture for better localization.
Main Methods:
- Utilized Fully Convolutional Networks (FCNs).
- Developed a novel FCN architecture integrating low-level and high-level features.
- Combined local and global information for enhanced localization.
Main Results:
- Achieved competitive performance in esophagus segmentation.
- Demonstrated improved localization accuracy through the proposed architecture.
- Experiments conducted on a dataset of 30 CT scans.
Conclusions:
- The proposed FCN architecture effectively improves esophagus segmentation.
- Integrating multi-level features enhances localization accuracy in low-contrast CT images.
- The method shows promise for radiotherapy treatment planning.
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