Keywords: AI Diffusion Models, Fetal, Segmentation performance, spina bifida
Motivation: Pathological fetal brains are difficult to segment due to limited annotated MRI data. Additionally, privacy concerns often restrict data sharing. Therefore, we need alternative augmentation techniques for improved segmentation of the pathological brain.
Goal(s): To generate realistic synthetic pathological fetal brain MRI data using generative AI and improve segmentation accuracy, specifically focusing on severe ventriculomegaly.
Approach: We trained a stable diffusion model on fetal MRI-label pairs, generated synthetic pathological MRI-labels derived from healthy MRIs through morphological alterations, and evaluated segmentation performance.
Results: The approach generated diverse high-quality synthetic pathological fetal brain MRIs and substantially improved segmentation performance, particularly for ventriculomegaly cases.
Impact: Our approach overcomes challenges of limited annotated pathological MRI datasets, facilitating the training of robust segmentation models without the need for pathological data. This advancement is an important step towards addressing privacy issues while improving segmentation performance in prenatal imaging.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords