Automating quantitative morphometric analysis of arteriolosclerotic blood vessels using machine learning
ArtS and SITE show promising potential for further optimization to create an assistive tool that enhances the current human-based semi-quantitative methods for assessment of vascular pathology.
Authors
- Michael J. Keiser (ORCID: https://orcid.org/0000-0002-1240-2192)
- Edwin S. Monuki (ORCID: https://orcid.org/0000-0001-5130-781X)
- J J Lou
- William H. Yong
- Shino Magaki (ORCID: https://orcid.org/0000-0003-0433-5759)
- Brittany N. Dugger (ORCID: https://orcid.org/0000-0003-2141-8855)
- D. Harvey
- Chanon Chantaduly
- Viharkumar Patel (ORCID: https://orcid.org/0000-0002-8541-6982)
- Elizabeth Head (ORCID: https://orcid.org/0000-0003-1115-6396)
- H V Vinters
- Hsin‐Pei Wang
- A. P. Chaudhari
- Peter Chang
- Charles Decarli
- Chen‐Nee Chuah
- Kiana D. Nava
Institutions
- University of California, Los Angeles (US)
- University of California, San Francisco (US)
- University of California, Irvine (US)
- Foundation for Psychocultural Research (US)
- Alzheimer’s Disease Neuroimaging Initiative (US)
- University of California, Davis (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70855_104156
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00