Unsupervised domain adaptation for bladder segmentation by U-nets in Cone Beam CT.
The main goal of this project is to accomplish the automatic segmentation of CBCT radiotherapy images using Deep Learning. Why? Because they are used in some process to treat cancer, in order to analyze where the radiation has to be applied. And we think that, by segmenting the images, the treatment...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/328218 |
| Acceso en línea: | https://hdl.handle.net/2117/328218 |
| Access Level: | acceso abierto |
| Palabra clave: | radiotherapy cancer medical image segmentation CT CBCT computed tomography bladder Unet deep learning artificial intelligence machine learning unsupervised domain adaptation |
| Sumario: | The main goal of this project is to accomplish the automatic segmentation of CBCT radiotherapy images using Deep Learning. Why? Because they are used in some process to treat cancer, in order to analyze where the radiation has to be applied. And we think that, by segmenting the images, the treatment applied to the patient could be executed more precisely, so the healthy tissues and organs around the tumor area would be less affected. This project is supervised by Benoît Macq and Eliott Brion, from ICTEAM research group at UCLouvain. |
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