Enhancing Brain Metastases Detection and Segmentation in Black-Blood MRI Using Deep Learning and Segment Anything Model (SAM)
The modifications to standard U-Net notably improves the detection and segmentation of BMs in BB images, and applying SAM as post-processing can further enhance the precision of segmentation results.
Authors
- Sang Kyun Yoo (ORCID: https://orcid.org/0000-0001-6967-0932)
- Eui Hyun Kim (ORCID: https://orcid.org/0000-0002-2523-7122)
- Hojin Kim (ORCID: https://orcid.org/0000-0002-4652-8682)
- Wonmo Sung (ORCID: https://orcid.org/0000-0002-0172-0646)
- Sung Soo Ahn (ORCID: https://orcid.org/0000-0002-0503-5558)
- Hong In Yoon (ORCID: https://orcid.org/0000-0002-2106-6856)
- Tae Hyung Kim (ORCID: https://orcid.org/0000-0002-5205-3775)
- Jin Sung Kim (ORCID: https://orcid.org/0000-0003-1415-6471)
Institutions
- Yonsei University (KR)
- Eulji University (KR)
- Severance Hospital (KR)
- Catholic University of Korea (KR)
Publication Details
- Journal
- Yonsei Medical Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.3349/ymj.2024.0198
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation
- Eulji University
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea