Brain Lesion Segmentation through Image Synthesis and Outlier Detection

Christopher Bowles, Chen Qin, Ricardo Guerrero, Roger Gunn, Alexander Hammers, David Alexander Dickie, Maria Valdes Hernandez, Joanna Wardlaw, Daniel Rueckert

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Cerebral small vessel disease (SVD) can manifest in a number of ways. Many of these result in hyperintense regions visible on T2-weighted magnetic resonance (MR) images. The automatic segmentation of these lesions has been the focus of many studies. However, previous methods tended to be limited to certain types of pathology, as a consequence of either restricting the search to the white matter, or by training on an individual pathology. Here we present an unsupervised abnormality detection method which is able to detect abnormally hyperintense regions on FLAIR regardless of the underlying pathology or location. The method uses a combination of image synthesis, Gaussian mixture models and one class support vector machines, and needs only be trained on healthy tissue. We evaluate our method by comparing segmentation results from 127 subjects with SVD with three established methods and report signifcantly superior performance across a number of metrics.
Original languageEnglish
Pages (from-to)643-658
JournalNeuroImage: Clinical
Early online date8 Sept 2017
Publication statusPublished - 2017


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