Keywords: White Matter, White Matter, CADASIL, WMH, longitudinal studies
Motivation: In CADASIL, the relationship between white matter hyperintensities (WMH) changes and disease progression remains unknown.
Goal(s): To estimate WMH changes, accurate segmentations are mandatory but remain challenging to obtain automatically because of patients MRI variability and heterogeneous contrast between WMH and normal appearing white matter. We aim to decrease correction time required to reach accurate results.
Approach: We used an incremental learning approach with a supervised algorithm from baseline segmentation data up to the final follow-up data to improve results and reduce manual correction time.
Results: A significant improvement of segmentation sensitivity and a reduction of manual correction time were obtained.
Impact: To accurately investigate the WMH progression in CADASIL, we propose an individual-based incremental training approach, with repeated learning of the segmentation algorithm from subsequent corrected data obtained along follow-up, to reduce the final manual correction time as much as possible.
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