  For patients suffering from brain tumor, prognosis estimation and treatment
decisions are made by a multidisciplinary team based on a set of preoperative
MR scans. Currently, the lack of standardized and automatic methods for tumor
detection and generation of clinical reports represents a major hurdle. In this
study, we investigate glioblastomas, lower grade gliomas, meningiomas, and
metastases, through four cohorts of up to 4000 patients. Tumor segmentation
models were trained using the AGU-Net architecture with different preprocessing
steps and protocols. Segmentation performances were assessed in-depth using a
wide-range of voxel and patient-wise metrics covering volume, distance, and
probabilistic aspects. Finally, two software solutions have been developed,
enabling an easy use of the trained models and standardized generation of
clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances
were quite homogeneous across the four different brain tumor types, with an
average true positive Dice ranging between 80% and 90%, patient-wise recall
between 88% and 98%, and patient-wise precision around 95%. With our Raidionics
software, running on a desktop computer with CPU support, tumor segmentation
can be performed in 16 to 54 seconds depending on the dimensions of the MRI
volume. For the generation of a standardized clinical report, including the
tumor segmentation and features computation, 5 to 15 minutes are necessary. All
trained models have been made open-access together with the source code for
both software solutions and validation metrics computation. In the future, an
automatic classification of the brain tumor type would be necessary to replace
manual user input. Finally, the inclusion of post-operative segmentation in
both software solutions will be key for generating complete post-operative
standardized clinical reports.
