Deep Learning-Based Assessment of Cerebral Microbleeds in COVID-19

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Cerebral Microbleeds (CMBs), typically captured as hypointensities from susceptibility-weighted imaging (SWI), are particularly important for the study of dementia, cerebrovascular disease, and normal aging. Recent studies on COVID-19 have shown an increase in CMBs of coronavirus cases. Automatic detection of CMBs is challenging due to the small size and amount of CMBs making the classes highly imbalanced, lack of publicly available annotated data, and similarity with CMB mimics such as calcifications, irons, and veins. Hence, the existing deep learning methods are mostly trained on very limited research data and fail to generalize to unseen data with high variability and cannot be used in clinical setups. To this end, we propose an efficient 3D deep learning framework that is actively trained on multi-domain data. Two public datasets assigned for normal aging, stroke, and Alzheimer's disease analysis as well as an in-house dataset for COVID-19 assessment are used to train and evaluate the models. The obtained results show that the proposed method is robust to low-resolution images and achieves 78% recall and 80% precision on the entire test set with an average false positive of 1.6 per scan.

Original languageEnglish
Title of host publication2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
Number of pages4
PublisherIEEE Computer Society Press
Publication date2023
ISBN (Electronic)9781665473583
Publication statusPublished - 2023
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duration: 18 Apr 202321 Apr 2023


Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
SponsorFlywheel, Kitware, Siemens Healthineers, UCLouvain
SeriesProceedings - International Symposium on Biomedical Imaging

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

    Research areas

  • cerebral microbleeds, COVID-19, Deep learning, precision-recall, susceptibility-weighted imaging

ID: 369551983