Deep learning models for separate segmentations of intracerebral and intraventricular hemorrhage on head CT and segmentation quality assessment
The proposed REUnet offers a promising tool for accurate and automated segmentation of ICH and IVH, and for effective IVH segmentation quality assessment, and thus exhibits the potential to facilitate therapeutic decision-making for patients with spontaneous ICH in clinical practice.
Authors
- Minghao Yin (ORCID: https://orcid.org/0000-0002-6226-2394)
- Shicai Zhang (ORCID: https://orcid.org/0000-0001-9414-9794)
- Meng Liang (ORCID: https://orcid.org/0000-0003-0916-520X)
- Xiankai Huo
- Zhenhe Liu (ORCID: https://orcid.org/0009-0000-1583-3273)
- Qian Wang (ORCID: https://orcid.org/0000-0002-7505-2951)
- Yifan Li (ORCID: https://orcid.org/0000-0003-3076-6632)
- Ruijie Zhang (ORCID: https://orcid.org/0000-0002-7293-9653)
- Xinbing Zuo
- Quan Zhang (ORCID: https://orcid.org/0009-0003-9518-5174)
Institutions
- Shandong University (CN)
- Tianjin Medical University General Hospital (CN)
- Tianjin Chest Hospital (CN)
- Qilu Hospital of Shandong University (CN)
- Tianjin Medical University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-08-12
- DOI
- https://doi.org/10.1002/mp.17343
- Citations
- 3
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 1.11
Funders
- National Natural Science Foundation of China
- Tianjin Medical University