  Tumor burden assessment by magnetic resonance imaging (MRI) is central to the
evaluation of treatment response for glioblastoma. This assessment is complex
to perform and associated with high variability due to the high heterogeneity
and complexity of the disease. In this work, we tackle this issue and propose a
deep learning pipeline for the fully automated end-to-end analysis of
glioblastoma patients. Our approach simultaneously identifies tumor
sub-regions, including the enhancing tumor, peritumoral edema and surgical
cavity in the first step, and then calculates the volumetric and bidimensional
measurements that follow the current Response Assessment in Neuro-Oncology
(RANO) criteria. Also, we introduce a rigorous manual annotation process which
was followed to delineate the tumor sub-regions by the human experts, and to
capture their segmentation confidences that are later used while training the
deep learning models. The results of our extensive experimental study performed
over 760 pre-operative and 504 post-operative adult patients with glioma
obtained from the public database (acquired at 19 sites in years 2021-2020) and
from a clinical treatment trial (47 and 69 sites for pre-/post-operative
patients, 2009-2011) and backed up with thorough quantitative, qualitative and
statistical analysis revealed that our pipeline performs accurate segmentation
of pre- and post-operative MRIs in a fraction of the manual delineation time
(up to 20 times faster than humans). The bidimensional and volumetric
measurements were in strong agreement with expert radiologists, and we showed
that RANO measurements are not always sufficient to quantify tumor burden.
