Machine learning-based pipeline for automated intracerebral hemorrhage and drain detection, quantification, and classification in non-enhanced CT images (NeuroDrAIn)
We developed and statistically validated an automated pipeline for evaluating computed tomography scans after minimally invasive surgery for intracerebral hemorrhage. The algorithm reliably detects drains, quantifies drain coverage by the hemorrhage, and uses machine learning to detect malpositioned drains. This pipeline has the potential to impact the daily clinical workload, as well as to facilitate the scaling of data collection for future research into intracerebral hemorrhage and other diseases.
Authors
- Horst Urbach (ORCID: https://orcid.org/0000-0001-7264-4807)
- Samer Elsheikh (ORCID: https://orcid.org/0000-0002-3796-5709)
- Urs Würtemberger (ORCID: https://orcid.org/0000-0002-2507-9160)
- Elias Kellner (ORCID: https://orcid.org/0000-0001-9494-2354)
- Theo Demerath (ORCID: https://orcid.org/0000-0002-5869-1110)
- Ahmed M. Elbaz (ORCID: https://orcid.org/0000-0002-2207-8912)
- Marco Reisert (ORCID: https://orcid.org/0000-0003-2742-1940)
- Ralf Watzlawick
- Alexander Rau (ORCID: https://orcid.org/0000-0001-5881-6043)
Institutions
- University of Freiburg (DE)
- University Medical Center Freiburg (DE)
Publication Details
- Journal
- PLoS ONE
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1371/journal.pone.0316003
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Eli Lilly and Company
- Biogen
- Bundesministerium für Bildung und Forschung
- Eisai