Carregant...
Miniatura

Tipus de document

Treball de fi de grau

Data de publicació

Llicència de publicació

cc-by-nc-nd (c) Lluís Pellicer Bolet, 2024
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/206942

Automatic segmentation of regions of interest in vaginal brachytherapy

Títol de la revista

ISSN de la revista

Títol del volum

Recurs relacionat

Resum

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.

Descripció

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

Citació

Citació

PELLICER BOLET, Lluís. Automatic segmentation of regions  of interest in vaginal brachytherapy. [consulta: 9 de gener de 2026]. [Disponible a: https://hdl.handle.net/2445/206942]

Exportar metadades

JSON - METS

Compartir registre