Unsupervised generative AI for enhancing brain tumor segmentation in multi-center, incomplete real-world data scenarios.

ABSTRACT: The clinical application of deep learning (DL)-based brain tumor segmentation remains limited by missing MRI sequences and cross-center data inconsistencies. Existing supervised generative models can synthesize missing sequences but rely on paired, fully sampled training data, which are often unavailable in routine practice. This study aims to assess the use of an unsupervised generative model to complete missing sequences and eliminate cross-center data inconsistency, and to verify whether using the generated images can enhance brain tumor segmentation. We retrospectively evaluated 921 glioblastoma (GBM) patients from BraTS, UCSF-PDGM, and our institutional datasets, together with 1000 meningioma cases from BraTS-MEN cohort. We developed an unsupervised multi-center multi-sequence generat

SUBMITTER: Li Z 

PROVIDER: S-EPMC12660812 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature