Keywords: Stroke, Machine Learning/Artificial Intelligence, segmentation, functional outcome, thrombectomyUsing a standard 3D nnUNET model that was pretrained to segment WMH on FLAIR, we obtained good stroke lesion segmentation accuracy (median Dice = 0.80) when the model was re-trained to segment the stroke lesion on DWI in a smaller stroke sample (N = 82). Applying the segmentation model to DWIs from 307 thrombectomy patients, we found no association between pre-treatment lesion volume and functional outcome at 90 days after thrombectomy. Our results indicate that the established assumption of lesion size being a strong predictor of functional outcome should be investigated further for patients receiving mechanical thrombectomy.
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