Omni-Supervised Domain Adversarial Training for White Matter Hyperintensity Segmentation in the UK Biobank

• Published in: Proceedings (International Symposium on Biomedical Imaging) pp. 1 - 4
• Main Authors: Sundaresan, Vaanathi, Dinsdale, Nicola K, Jenkinson, Mark, Griffanti, Ludovica
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.03.2022
• Subjects: adversarial training; Biological system modeling; Codes; domain adaptation; Image segmentation; Multitasking; omni-supervised learning; Predictive models; Training; Training data; UK Biobank; white matter hyperintensities
• DOI: 10.1109/ISBI52829.2022.9761539

White matter hyperintensities (WMHs, or lesions) appear as hyperintense, localized regions on T2-weighted and FLAIR brain MR images. The heterogeneity in lesion characteristics due to subject-level (e.g., local intensity/contrast) and population-level (e.g., demographic, scanner-related) variations make their segmentation highly challenging. Here, we propose a framework for adapting a state-of-the-art WMH segmentation method with high accuracy from a small, labeled source data (MICCAI WMH segmentation challenge 2017 training data) to a larger dataset such as the UK Biobank without the need of additional manual training labels, using domain adversarial training with omni-supervised learning. Given the well-known association of WMHs with age, the proposed method uses a multi-tasking model for learning lesion segmentation, domain adaptation and age prediction simultaneously. On a subset of the UK Biobank dataset, the proposed method achieves a lesion-level recall, lesion-level F1-measure and Dice overlap value of 0.95, 0.65 and 0.84 respectively, when compared to values of 0.75, 0.49 and 0.80 obtained from the pretrained state-of-the-art baseline method. The code for the method is available at https://github.com/v-sundaresan/omnisup_agepred_semidann.