  Automatic and consistent meningioma segmentation in T1-weighted MRI volumes
and corresponding volumetric assessment is of use for diagnosis, treatment
planning, and tumor growth evaluation. In this paper, we optimized the
segmentation and processing speed performances using a large number of both
surgically treated meningiomas and untreated meningiomas followed at the
outpatient clinic. We studied two different 3D neural network architectures:
(i) a simple encoder-decoder similar to a 3D U-Net, and (ii) a lightweight
multi-scale architecture (PLS-Net). In addition, we studied the impact of
different training schemes. For the validation studies, we used 698 T1-weighted
MR volumes from St. Olav University Hospital, Trondheim, Norway. The models
were evaluated in terms of detection accuracy, segmentation accuracy and
training/inference speed. While both architectures reached a similar Dice score
of 70% on average, the PLS-Net was more accurate with an F1-score of up to 88%.
The highest accuracy was achieved for the largest meningiomas. Speed-wise, the
PLS-Net architecture tended to converge in about 50 hours while 130 hours were
necessary for U-Net. Inference with PLS-Net takes less than a second on GPU and
about 15 seconds on CPU. Overall, with the use of mixed precision training, it
was possible to train competitive segmentation models in a relatively short
amount of time using the lightweight PLS-Net architecture. In the future, the
focus should be brought toward the segmentation of small meningiomas (less than
2ml) to improve clinical relevance for automatic and early diagnosis as well as
speed of growth estimates.
