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MICCAI 2022 Workshop on Machine Learning in Clinical Neuroimaging

Volume is All You Need: Improving Multi-task Multiple Instance Learning for WMH Segmentation and Severity Estimation

  • Oct. 2022

Abstract

White matter hyperintensities (WMHs) are lesions with unusually high intensity detected in T2 fluid-attenuated inversion recovery (T2-FLAIR) MRI images, commonly attributed to vascular dementia (VaD) and chronic small vessel ischaemia. The Fazekas scale is a measure of WMH severity, widely used in radiology research. Although stand-alone WMH segmentation methods have been extensively investigated, a model encapsulating both WMH segmentation and Fazekas scale prediction has not. We propose a novel multi-task multiple instance learning (MTMIL) model for simultaneous WMH lesions segmentation and Fazekas scale estimation. The model is initially trained only for the segmentation task to overcome the difficulty of the manual annotation process. Afterwards, volume-guided attention (VGA) obtained directly from instance-level segmentation results figure out key instances for the classification task. We trained the model with 558 in-house brain MRI data, where only 58 of them have WMH annotations. Our MTMIL method reinforced by segmentation results outperforms other multiple instance learning methods.

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