Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholding
Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholding
Abstract White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factors and neurode-generative diseases affect lesion load and spatial distribution. At the individual level, WMH vary in contrast, amount and distribution in different white matter regions. In this work, we aimed to improve BIANCA, the FSL tool for WMH segmentation, in order to better deal with these sources of variability. We worked on two stages of BIANCA by improving the lesion probability map estimation (classification stage) and making the lesion probability map thresholding stage automated and adaptive to local lesion probabilities. Firstly, in order to take into account the effect of population-level factors, we included population-level lesion probabilities, modelled with respect to a parametric factor (e.g. age), in the classification stage. Secondly, we tested BIANCA performance when using four alternative classifiers commonly used in the literature, with respect to K-nearest neighbour algorithm currently used for lesion probability map estimation in BIANCA. Finally, we propose LOCally Adaptive Threshold Estimation (LOCATE), a supervised method for determining optimal local thresholds to apply to the estimated lesion probability map, as an alternative option to global thresholding (i.e. applying the same threshold to the entire lesion probability map). For these experiments we used data from a neurodegenerative cohort and a vascular cohort. We observed that including population-level parametric lesion probabilities with re-spect to age and using alternative machine learning techniques provided negligible im-provement. However, LOCATE provided a substantial improvement in the lesion segmentation performance when compared to the global thresholding currently used in BIANCA. We further validated LOCATE on a cohort of CADASIL (Cerebral autoso-mal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) patients, a genetic form of cerebral small vessel disease characterised by extensive WMH burden, and healthy controls showing that LOCATE adapts well to wide variations in lesion load and spatial distribution.
Jenkinson Mark、De Stefano Nicola、Rothwell Peter M.、Husain Masud、Battaglini Marco、Le Heron Campbell、Sundaresan Vaanathi、Zamboni Giovanna、Griffanti Ludovica
Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain,Nuffield Department of Clinical Neurosciences, University of OxfordDepartment of Medicine, Surgery and Neuroscience, University of SienaCentre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of OxfordNffield Department of Clinical Neurosciences, University of Oxford||Department of Experimental Psychology, University of Oxford||Wellcome Centre for Integrative NeuroImaging, University of OxfordDepartment of Medicine, Surgery and Neuroscience, University of SienaNffield Department of Clinical Neurosciences, University of Oxford||New Zealand Brain Research InstituteWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain,Nuffield Department of Clinical Neurosciences, University of Oxford||Oxford-Nottingham Centre for Doctoral Training in Biomedical Imaging, University of Oxford||Oxford India Centre for Sustainable Development, Somerville College, University of OxfordWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain,Nuffield Department of Clinical Neurosciences, University of Oxford||Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of OxfordWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain,Nuffield Department of Clinical Neurosciences, University of Oxford
医学研究方法神经病学、精神病学基础医学
White matter hyperintensitiesstructural MRIlesion probability mapThresholdingMachine learninglesion segmentation
Jenkinson Mark,De Stefano Nicola,Rothwell Peter M.,Husain Masud,Battaglini Marco,Le Heron Campbell,Sundaresan Vaanathi,Zamboni Giovanna,Griffanti Ludovica.Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholding[EB/OL].(2025-03-28)[2025-05-11].https://www.biorxiv.org/content/10.1101/437608.点此复制
评论