Meeting Banner
Abstract #4329

Automated IDH genotype prediction pipeline using multimodal domain adaptive segmentation (MDAS) model

Hailong Zeng1, Lina Xu1, Zhen Xing2, Wanrong Huang2, Yan Su2, Zhong Chen1, Dairong Cao2, Zhigang Wu3, Shuhui Cai1, and Congbo Cai1
1Department of Electronic Science, Xiamen University, Xiamen, China, 2Department of Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China, 3MSC Clinical & Technical Solutions, Philips Healthcare, ShenZhen, China


Mutation status of isocitrate dehydrogenase (IDH) in gliomas exhibits distinct prognosis. It poses challenges to jointly perform tumor segmentation and gene prediction directly using label-deprived multi-parametric MR images from clinics . We propose a novel multimodal domain adaptive segmentation (MDAS) framework, which derives unsupervised segmentation of tumor foci by learning data distribution between public dataset with labels and label-free targeted dataset. High-level features of radiomics and deep network are combined to manage IDH subtyping. Experiments demonstrate that our method adaptively aligns dataset from both domains with more tolerance toward distribution discrepancy during segmentation procedure and obtains competitive genotype prediction performance.

This abstract and the presentation materials are available to members only; a login is required.

Join Here