Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.

ABSTRACT: Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice scor

SUBMITTER: Helland RH 

PROVIDER: S-EPMC10622432 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature