Development and validation of a deep learning-based automatic segmentation and classification of cerebral white matter hyperintensities.

objectivesWhite matter hyperintensities (WMH) represent neuroimaging markers of cerebral small vessel disease. We aimed to develop and validate a deep learning-based simultaneous, automatic WMH segmentation and classification model in patients with cognitive impairment. MATERIALS AND

methodsThis retrospective study included images from consecutive patients with cognitive impairment from a tertiary hospital. A segmentation model was trained and tuned on 448 and 149 subjects. A classification compartment inherited from the segmentation model, utilizing the multi-task multiple instance learning framework (MTMIL), was trained and tuned on 1186 and 394 subjects. Tests of segmentation and classification tasks were performed on 149 and 394 subjects in the internal testing dataset and 100 subjects in the external dataset. We evaluated five different models to select the segmentation branch. Classification according to the Fazekas scale used three categories (normal/mild, moderate, severe), and locations were separately reported as periventricular and deep WMH. For classification evaluation, three experienced neuroradiologists evaluated test datasets.

resultsBetween January 2016 and December 2019, 1974 consecutive patients (mean age 71.1 ± 9.7) were included. Dice score performance of the UNet with Resnet-34 encoder model on internal and external testing datasets was 0.88 (95% CI: 0.88-0.89) and 0.85 (95% CI: 0.84-0.86) for single-class segmentation, and 0.77 (95% CI: 0.76-0.79) and 0.72 (95% CI: 0.71-0.74) for multi-class segmentation. The accuracy of the Fazekas scale classification model was 0.88 and 0.87 at periventricular and deep WMH with internal datasets and 0.68 and 0.75 with external datasets.

conclusionThese results demonstrate the high segmentation and classification performance of our models and their potential for deployment as accurate diagnostic support tools for quantified evaluation of WMH. KEY POINTS: Question We developed a deep learning-based, simultaneous, automatic white matter hyperintensity (WMH) segmentation and classification model using data from patients with cognitive impairment. Findings Segmentation model achieved Dice scores of 0.72-0.88 for single-class and multi-class segmentation. Fazekas score classification accuracy ranged from 0.68 to 0.88 for periventricular/deep WMH. Clinical relevance Our study demonstrated high segmentation and classification performance of deep learning-based models and potential for deployment as accurate diagnostic support tools for quantified evaluation of white matter hyperintensities.