  Meningiomas are the most common type of primary brain tumor, accounting for
approximately 30% of all brain tumors. A substantial number of these tumors are
never surgically removed but rather monitored over time. Automatic and precise
meningioma segmentation is therefore beneficial to enable reliable growth
estimation and patient-specific treatment planning. In this study, we propose
the inclusion of attention mechanisms over a U-Net architecture: (i)
Attention-gated U-Net (AGUNet) and (ii) Dual Attention U-Net (DAUNet), using a
3D MRI volume as input. Attention has the potential to leverage the global
context and identify features' relationships across the entire volume. To limit
spatial resolution degradation and loss of detail inherent to encoder-decoder
architectures, we studied the impact of multi-scale input and deep supervision
components. The proposed architectures are trainable end-to-end and each
concept can be seamlessly disabled for ablation studies. The validation studies
were performed using a 5-fold cross validation over 600 T1-weighted MRI volumes
from St. Olavs University Hospital, Trondheim, Norway. For the best performing
architecture, an average Dice score of 81.6% was reached for an F1-score of
95.6%. With an almost perfect precision of 98%, meningiomas smaller than 3ml
were occasionally missed hence reaching an overall recall of 93%. Leveraging
global context from a 3D MRI volume provided the best performances, even if the
native volume resolution could not be processed directly. Overall, near-perfect
detection was achieved for meningiomas larger than 3ml which is relevant for
clinical use. In the future, the use of multi-scale designs and refinement
networks should be further investigated to improve the performance. A larger
number of cases with meningiomas below 3ml might also be needed to improve the
performance for the smallest tumors.
