Maria del C. Valdes Hernandez

Ben Philps, Maria del C. Valdes Hernandez, Chen Qin et al.

White Matter Hyperintensities (WMH) are key neuroradiological markers of small vessel disease present in brain MRI. Assessment of WMH is important in research and clinics. However, WMH are challenging to segment due to their high variability in shape, location, size, poorly defined borders, and similar intensity profile to other pathologies (e.g stroke lesions) and artefacts (e.g head motion). In this work, we assess the utility and semantic properties of the most effective techniques for uncertainty quantification (UQ) in segmentation for the WMH segmentation task across multiple test-time data distributions. We find UQ techniques reduce 'silent failure' by identifying in UQ maps small WMH clusters in the deep white matter that are unsegmented by the model. A combination of Stochastic Segmentation Networks with Deep Ensembles also yields the highest Dice and lowest Absolute Volume Difference % (AVD) score and can highlight areas where there is ambiguity between WMH and stroke lesions. We further demonstrate the downstream utility of UQ, proposing a novel method for classification of the clinical Fazekas score using spatial features extracted from voxelwise WMH probability and UQ maps. We show that incorporating WMH uncertainty information improves Fazekas classification performance and calibration. Our model with (UQ and spatial WMH features)/(spatial WMH features)/(WMH volume only) achieves a balanced accuracy score of 0.74/0.67/0.62, and root brier score of 0.65/0.72/0.74 in the Deep WMH and balanced accuracy of 0.74/0.73/0.71 and root brier score of 0.64/0.66/0.68 in the Periventricular region. We further demonstrate that stochastic UQ techniques with high sample diversity can improve the detection of poor quality segmentations.

Enrico Pellegrini, Lucia Ballerini, Maria del C. Valdes Hernandez et al.

INTRODUCTION: Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear. METHODS: We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy ageing through to dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries. RESULTS: Of 111 relevant studies, most assessed Alzheimer's disease (AD) vs healthy controls, used ADNI data, support vector machines and only T1-weighted sequences. Accuracy was highest for differentiating AD from healthy controls, and poor for differentiating healthy controls vs MCI vs AD, or MCI converters vs non-converters. Accuracy increased using combined data types, but not by data source, sample size or machine learning method. DISCUSSION: Machine learning does not differentiate clinically-relevant disease categories yet. More diverse datasets, combinations of different types of data, and close clinical integration of machine learning would help to advance the field.