Keywords: Neuroinflammation, Segmentation
Motivation: To automate rim lesion segmentation in multiple sclerosis
Goal(s): To compare deep learning and conventional methods for rim lesion segmentation in multiple sclerosis
Approach: We compare Unet with chan-vese and Grabcut segmention of MR rim positive lesions.
Results: Deep learning achieve the highest Dice score among the compared methods.
Impact: Automate rim lesion segmentation in Multiple Sclerosis may allow determine those patient with persistent inflammation.
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