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 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.

arXiv comment9 pages, 4 figures, submitted to MICCAI

References in corpus (3)

• Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
• NiftyNet: a deep-learning platform for medical imaging
• Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

Cited by in corpus (6)

• Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention
• Scribble-based Domain Adaptation via Co-segmentation
• Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation
• Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks
• Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion
• 3D Distance-color-coded Assessment of PCI Stent Apposition via Deep-learning-based Three-dimensional Multi-object Segmentation