Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss

Abstract

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 propose an attention module with explicit supervision on the attention maps to enable the CNN to focus on the small target for more accurate segmentation. We also 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) our 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 improves the segmentation accuracy. 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.