Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice

Aakanksha Rana

Abstract:

Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient
follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual
labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms
for meningioma segmentation have the potential to bring volumetric analysis into clinical and
research workfows by increasing accuracy and efciency, reducing inter-user variability and saving
time. Previous research has focused solely on segmentation tasks without assessment of impact and
usability of deep learning solutions in clinical practice. Herein, we demonstrate a three-dimensional
convolutional neural network (3D-CNN) that performs expert-level, automated meningioma
segmentation and volume estimation on MRI scans. A 3D-CNN was initially trained by segmenting
entire brain volumes using a dataset of 10,099 healthy brain MRIs. Using transfer learning, the
network was then specifcally trained on meningioma segmentation using 806 expert-labeled MRIs.
The fnal model achieved a median performance of 88.2% reaching the spectrum of current interexpert variability (82.6–91.6%). We demonstrate in a simulated clinical scenario that a deep learning
approach to meningioma segmentation is feasible, highly accurate and has the potential to improve
current clinical practice.