Towards reliable brain tumor segmentation in MRI neuroimaging: integrating uncertainty estimation and ensemble methods for clinical applications
This thesis addresses uncertainty in automated brain tumor segmentation using deep learning. An ensemble of SwinUNETR, SegResNet, and Attention U-Net models was developed, incorporating test-time augmentation and test-time dropout for uncertainty estimation. Evaluation on the BraTS 2021 dataset show...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| 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:2117/430325 |
| Acceso en línea: | https://hdl.handle.net/2117/430325 |
| Access Level: | acceso abierto |
| Palabra clave: | Deep learning (Machine learning) Imaging systems in medicine Image segmentation Brain--Tumors Segmentació automatitzada de tumors cerebrals Ensembles d'aprenentatge profund Estimació de la incertesa Augmentació en temps d'inferència Dropout en temps d'inferència SwinUNETR SegResNet Attention U-Net Predicció conscient del risc BraTS 2021 Segmentació d'imatges mèdiques Automated brain tumor segmentation Deep learning ensembles Uncertainty estimation Test-time augmentation Test-time dropout Risk-aware prediction Medical image segmentation Aprenentatge profund Imatgeria mèdica Imatges--Segmentació Cervell--Tumors Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | This thesis addresses uncertainty in automated brain tumor segmentation using deep learning. An ensemble of SwinUNETR, SegResNet, and Attention U-Net models was developed, incorporating test-time augmentation and test-time dropout for uncertainty estimation. Evaluation on the BraTS 2021 dataset showed that the ensemble achieved statistically significant improvements in segmentation accuracy, particularly for edema, relative to individual models. Uncertainty maps correlated with voxel-wise error and enabled risk-aware prediction. A Streamlit application was prototyped for clinical feasibility, allowing for interactive visualization of segmentation and uncertainty. The developed application lays the theoretical and technical groundwork for future compliance studies in medical image segmentation, with the potential to accelerate regulatory approval and clinical integration. Further progress will require larger, more diverse models and prospective reader studies to translate these preliminary findings into clinically meaningful improvements. |
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