Keywords: Segmentation, Alzheimer's Disease, Multi-Contrast, AISegmentation of the hippocampus on T1-weighted structural MR images is required to quantify the neurodegenerative effects in Alzheimer’s disease studies. In this work, we propose an automated artificial intelligence-based pipeline for hippocampus segmentation combined with manual ground truth (GT) data that originates from high-resolution T2-weighted MR images. Results are evaluated against the manual GT-labels and compared to the segmentation results from FreeSurfer v732. Our deep learning-based segmentation outperforms FreeSurfer in terms of accuracy and speed, while reference experiments using the T2-based GT-labels yield the best results. Thus, using T2-weighted images for ground truth generation can improve automated HC segmentation.
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