Automatic segmentation of brain structures in magnetic resonance images using deep learning techniques
This PhD thesis focuses on the development of deep learning based methods for accurate segmentation of the sub-cortical brain structures from MRI. First, we have proposed a 2.5D CNN architecture that combines convolutional and 2/2 spatial features. Second, we proposed a supervised domain adaptation...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/670766 |
| Acceso en línea: | http://hdl.handle.net/10803/670766 |
| Access Level: | acceso abierto |
| Palabra clave: | Brain structures Estructures cerebrals Estructuras cerebrales MRI Magnetic resonance imaging Imatgeria per ressonància magnètica Imágenes por resonancia magnética Deep learning Aprenentatge profund Aprendizaje profundo Subcortical structures Estructures subcorticals Estructuras subcorticales CNN Convolutional neural network Xarxa neuronal convolucional Red neuronal convolucional 004 616.8 |
| Sumario: | This PhD thesis focuses on the development of deep learning based methods for accurate segmentation of the sub-cortical brain structures from MRI. First, we have proposed a 2.5D CNN architecture that combines convolutional and 2/2 spatial features. Second, we proposed a supervised domain adaptation technique to improve the robustness and consistency of deep learning model. Third, an unsupervised domain adaptation method was proposed to eliminate the requirement of manual intervention to train a deep learning model that is robust to differences in the MRI images from multi-centre and multi-scanner datasets. The experimental results for all the proposals demonstrated the effectiveness of our approaches in accurately segmenting the sub-cortical brain structures and has shown state-of-the-art performance on well-known publicly available datasets |
|---|