Paper
21 June 2024 Human contour segmentation algorithm for radiotherapy-assisted positioning
Yihang Lin, Yuanzhang Wang, Guansen Hua
Author Affiliations +
Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131670B (2024) https://doi.org/10.1117/12.3029615
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China
Abstract
In this paper, a body contour segmentation algorithm for assisting radiotherapy patient posing is investigated. The image data of radiotherapy patients with body parts are captured using an RGB-D camera, and the U2-Net network model is improved to segment the body contour of radiotherapy patients wearing thermoplastic membranes and fixed stents by adding CBAM and CA attention mechanisms. The body contours acquired in the CT simulation localization room are displayed in the computer equipment in the radiotherapy room, thus helping the radiotherapist to restore the real-time posture of the patient in the radiotherapy room approximately to the same position in the CT localization room. The results show that the evaluation indexes of this segmentation algorithm can reach 98.7% for PA, 95.8% for IOU, and 0.969 for DSC, which can achieve a better segmentation effect, and the extracted patient contour is more accurate, smooth, and less noisy. Therefore, the algorithm can help radiotherapists to accomplish the task of assisted posing.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yihang Lin, Yuanzhang Wang, and Guansen Hua "Human contour segmentation algorithm for radiotherapy-assisted positioning", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131670B (21 June 2024); https://doi.org/10.1117/12.3029615
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KEYWORDS
Image segmentation

Radiotherapy

Data modeling

Contour modeling

Deep learning

Medical imaging

Computed tomography

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