Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, Multi-Atlas Segmentation
Motivation: Multi-atlas segmentation (MAS) of MR brain images with lesions is of great clinical significance but remains challenging due to registration inaccuracy caused by pathologies.
Goal(s): Our goal was to improve the MAS performance of pathological brain images by restoring more accurate normal images form lesion data.
Approach: We integrate a novel subspace-assisted generative model into the MAS framework for estimation of subject-specific posterior normative distribution, which can effectively extract a “hypothetical” normal image from the lesion data, thus enhancing the accuracy of lesion segmentation.
Results: Our method produced significantly improved results in normal recovery and MAS compared to the state-of-the-art methods.
Impact: The proposed method significantly improves the performance of segmentation of MR brain images with lesions, which may provide a useful tool for tissue segmentation in pathological brain images.
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