  Automatic segmentation of vestibular schwannoma (VS) tumors from magnetic
resonance imaging (MRI) would facilitate efficient and accurate volume
measurement to guide patient management and improve clinical workflow. The
accuracy and robustness is challenged by low contrast, small target region and
low through-plane resolution. We introduce a 2.5D convolutional neural network
(CNN) able to exploit the different in-plane and through-plane resolutions
encountered in standard of care imaging protocols. We use an attention module
to enable the CNN to focus on the small target and propose a supervision on the
learning of attention maps for more accurate segmentation. Additionally, we
propose a hardness-weighted Dice loss function that gives higher weights to
harder voxels to boost the training of CNNs. Experiments with ablation studies
on the VS tumor segmentation task show that: 1) the proposed 2.5D CNN
outperforms its 2D and 3D counterparts, 2) our supervised attention mechanism
outperforms unsupervised attention, 3) the voxel-level hardness-weighted Dice
loss can improve the performance of CNNs. Our method achieved an average Dice
score and ASSD of 0.87 and 0.43~mm respectively. This will facilitate patient
management decisions in clinical practice.
