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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/206942
Automatic segmentation of regions of interest in vaginal brachytherapy
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Abstract
The postoperative endometrial carcinoma treatment often includes radiotherapy (external
radiotherapy and/or vaginal brachytherapy) to prevent the reappearance of the tumour. This project
aims to improve the efficiency of the vaginal brachytherapy treatment by developing an automatic
segmentation algorithm capable of delineating both the clinical target volume and the organs at
risk, reducing the time required by experts to exert such task.
In this project, we develop an AI-based framework that uses a V-Net architecture at its core. To
train and evaluate the model, we use retrospective CT images and corresponding manual
delineations from patients treated in Hospital Clinic.
The creation of the algorithm was achieved successfully, resulting in a completely functional creator
of automatic segmentations. About its performance, the results were found satisfactory in the cases
of the vagina, the rectum and the bladder, having acceptable discrepancies in the dosimetry output.
On the other hand, the bowel and the sigma models would require further improvements since the
segmentations obtained didn’t match the ground truth.
Overall, the project represents a step forward in the application of artificial intelligence algorithms
to radiotherapy related processes.
Description
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2023-2024. Tutor: Aida Niñerola ; Director: Adrià Casamitjana
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Citation
PELLICER BOLET, Lluís. Automatic segmentation of regions of interest in vaginal brachytherapy. [consulted: 13 of June of 2026]. Available at: https://hdl.handle.net/2445/206942