# name
Automatic segmentation of regions  of interest in vaginal brachytherapy

# metadata.dc.description[].value
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

# metadata.dc.description.abstract[].value
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.

# metadata.dc.rights[].value
cc-by-nc-nd (c) Lluís Pellicer Bolet, 2024

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Enginyeria Biomèdica

# metadata.dc.title[].value
Automatic segmentation of regions  of interest in vaginal brachytherapy