| Autor: |
Canalini, Luca, Klein, Jan, de Barros, Nuno Pedrosa, Sima, Diana Maria, Miller, Dorothea, Hahn, Horst |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
SPIE Proceedings Vol. 11598 - Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1117/12.2580889 |
| Popis: |
In this work, we compare five deep learning solutions to automatically segment the resection cavity in postoperative MRI. The proposed methods are based on the same 3D U-Net architecture. We use a dataset of postoperative MRI volumes, each including four MRI sequences and the ground truth of the corresponding resection cavity. Four solutions are trained with a different MRI sequence. Besides, a method designed with all the available sequences is also presented. Our experiments show that the method trained only with the T1 weighted contrast-enhanced MRI sequence achieves the best results, with a median DICE index of 0.81. |
| Databáze: |
arXiv |
| Externí odkaz: |
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