  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 score, and the best classification
performance was about 80\% balanced accuracy, with a demonstrated ability to
generalize across hospitals. In addition, the segmentation performance of the
best models was on par with human expert raters. The predicted segmentations
can be used to accurately classify the patients into those with residual tumor,
and those with gross total resection.
