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VASARI-auto: equitable, efficient, and economical featurisation of glioma MRI
April 25, 2024, 7:45 p.m. | James K Ruffle, Samia Mohinta, Kelly Pegoretti Baruteau, Rebekah Rajiah, Faith Lee, Sebastian Brandner, Parashkev Nachev, Harpreet Hyare
cs.CV updates on arXiv.org arxiv.org
Abstract: The VASARI MRI feature set is a quantitative system designed to standardise glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and seldom used in clinical practice. This is a problem that machine learning could plausibly automate. Using glioma data from 1172 patients, we developed VASARI-auto, an automated labelling software applied to both open-source lesion masks and our openly available tumour segmentation model. In parallel, two consultant neuroradiologists independently quantified VASARI features in a subsample …
abstract arxiv auto automate clinical cs.cv data feature imaging machine machine learning mri patients practice q-bio.qm q-bio.to quantitative set type
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