Format

Send to

Choose Destination
Neuroimage. 2019 Jul 31;202:116056. doi: 10.1016/j.neuroimage.2019.116056. [Epub ahead of print]

Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding.

Author information

1
Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Oxford-Nottingham Centre for Doctoral Training in Biomedical Imaging, University of Oxford, UK; Oxford India Centre for Sustainable Development, Somerville College, University of Oxford, UK. Electronic address: vaanathi.sundaresan@dtc.ox.ac.uk.
2
Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
3
Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; New Zealand Brain Research Institute, Christchurch 8011, New Zealand.
4
Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
5
Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Experimental Psychology, University of Oxford, Oxford, UK; Wellcome Centre for Integrative NeuroImaging, University of Oxford, UK.
6
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
7
Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.

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 neurodegenerative 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, a vascular cohort and the cohorts available publicly as a part of a segmentation challenge. We observed that including population-level parametric lesion probabilities with respect to age and using alternative machine learning techniques provided negligible improvement. However, LOCATE provided a substantial improvement in the lesion segmentation performance, when compared to the global thresholding. It allowed to detect more deep lesions and provided better segmentation of periventricular lesion boundaries, despite the differences in the lesion spatial distribution and load across datasets. We further validated LOCATE on a cohort of CADASIL (Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) patients, a genetic form of cerebral small vessel disease, and healthy controls, showing that LOCATE adapts well to wide variations in lesion load and spatial distribution.

KEYWORDS:

Lesion probability map; Lesion segmentation; Machine learning; Structural MRI; Thresholding; White matter hyperintensities

Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center