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Andres-Rodriguez-Arnau.pdf (638.83 KB)

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Bachelor thesis

Publication date

2023-06

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cc-by-nc-nd (c) Andrés, 2023
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/200775

Automatic segmentation of regions of interest with Deep Learning for postoperative endometrial carcinoma treatment

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Abstract

This project aims to evaluate deep learning algorithms’ suitability to correctly delineate the regions of interest on computer tomography images for dosimetric computations, in the context of postoperative endometrial carcinoma treatment. To achieve this goal, the project includes the complete training and evaluation of two deep learning networks. Furthermore, a qualitative assessment of the predicted dosimetric computations and a post-processing of the predicted results have been conducted

Description

Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2023, Tutora: Aida Niñerola Baizan

Subject

Aprenentatge profund, Tomografia, Càncer d'endometri, Treballs de fi de grau

Subject (English)

Deep learning, Tomography, Endometrial cancer, Bachelor's theses

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Treballs Finals de Grau (TFG) - Física

Citation

ANDRÉS RODRÍGUEZ, Arnau. Automatic segmentation of regions of interest with Deep Learning for postoperative endometrial carcinoma treatment. [consulted: 12 of August of 2026]. Available at: https://hdl.handle.net/2445/200775

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