Fully Automated Enhanced Tumor Compartmentalization: Man vs. Machine Reloaded.
OBJECTIVE
Comparison of a fully-automated segmentation method that uses compartmental volume information to a semi-automatic user-guided and FDA-approved segmentation technique.


METHODS
Nineteen patients with a recently diagnosed and histologically confirmed glioblastoma (GBM) were included and MR images were acquired with a 1.5 T MR scanner. Manual segmentation for volumetric analyses was performed using the open source software 3D Slicer version 4.2.2.3 (www.slicer.org). Semi-automatic segmentation was done by four independent neurosurgeons and neuroradiologists using the computer-assisted segmentation tool SmartBrush® (referred to as SB), a semi-automatic user-guided and FDA-approved tumor-outlining program that uses contour expansion. Fully automatic segmentations were performed with the Brain Tumor Image Analysis (BraTumIA, referred to as BT) software. We compared manual (ground truth, referred to as GT), computer-assisted (SB) and fully-automated (BT) segmentations with regard to: (1) products of two maximum diameters for 2D measurements, (2) the Dice coefficient, (3) the positive predictive value, (4) the sensitivity and (5) the volume error.


RESULTS
Segmentations by the four expert raters resulted in a mean Dice coefficient between 0.72 and 0.77 using SB. BT achieved a mean Dice coefficient of 0.68. Significant differences were found for intermodal (BT vs. SB) and for intramodal (four SB expert raters) performances. The BT and SB segmentations of the contrast-enhancing volumes achieved a high correlation with the GT. Pearson correlation was 0.8 for BT; however, there were a few discrepancies between raters (BT and SB 1 only). Additional non-enhancing tumor tissue extending the SB volumes was found with BT in 16/19 cases. The clinically motivated sum of products of diameters measure (SPD) revealed neither significant intermodal nor intramodal variations. The analysis time for the four expert raters was faster (1 minute and 47 seconds to 3 minutes and 39 seconds) than with BT (5 minutes).


CONCLUSIONS
BT and SB provide comparable segmentation results in a clinical setting. SB provided similar SPD measures to BT and GT, but differed in the volume analysis in one of the four clinical raters. A major strength of BT may its independence from human interactions, it can thus be employed to handle large datasets and to associate tumor volumes with clinical and/or molecular datasets ("-omics") as well as for clinical analyses of brain tumor compartment volumes as baseline outcome parameters. Due to its multi-compartment segmentation it may provide information about GBM subcompartment compositions that may be subjected to clinical studies to investigate the delineation of the target volumes for adjuvant therapies in the future.
Support Center for Advanced Neuroimaging-Institute for Diagnostic and Interventional Neuroradiology, University Hospital Inselspital and University of Bern, Bern, Switzerland.|Department of Neurosurgery, University Hospital Inselspital and University of Bern, Bern, Switzerland.
Nicole
http://orcid.org/0000-0003-1607-1965
N
Porz
Support Center for Advanced Neuroimaging-Institute for Diagnostic and Interventional Neuroradiology, University Hospital Inselspital and University of Bern, Bern, Switzerland.
Simon
S
Habegger
Institute of Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland.
Raphael
R
Meier
Support Center for Advanced Neuroimaging-Institute for Diagnostic and Interventional Neuroradiology, University Hospital Inselspital and University of Bern, Bern, Switzerland.
Rajeev
R
Verma
Department of Neurosurgery, University Hospital Inselspital and University of Bern, Bern, Switzerland.
Astrid
A
Jilch
Department of Neurosurgery, University Hospital Inselspital and University of Bern, Bern, Switzerland.
Jens
J
Fichtner
Support Center for Advanced Neuroimaging-Institute for Diagnostic and Interventional Neuroradiology, University Hospital Inselspital and University of Bern, Bern, Switzerland.
Urspeter
U
Knecht
Department of Neurosurgery, University Hospital Inselspital and University of Bern, Bern, Switzerland.
Christian
C
Radina
Department of Neurosurgery, University Hospital Inselspital and University of Bern, Bern, Switzerland.
Philippe
P
Schucht
Department of Neurosurgery, University Hospital Inselspital and University of Bern, Bern, Switzerland.
Jürgen
J
Beck
Department of Neurosurgery, University Hospital Inselspital and University of Bern, Bern, Switzerland.
Andreas
A
Raabe
Support Center for Advanced Neuroimaging-Institute for Diagnostic and Interventional Neuroradiology, University Hospital Inselspital and University of Bern, Bern, Switzerland.
Johannes
J
Slotboom
Institute of Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland.
Mauricio
M
Reyes
Support Center for Advanced Neuroimaging-Institute for Diagnostic and Interventional Neuroradiology, University Hospital Inselspital and University of Bern, Bern, Switzerland.
Roland
R
Wiest
D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D005260:Female; D005909:Glioblastoma / Q000000981:diagnostic imaging / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D047368:Tumor Burden