期刊
FRONTIERS IN PHYSIOLOGY
卷 12, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fphys.2021.709230
关键词
cardiac magnetic resonance; late gadolinium enhancement; scar segmentation; deep learning; atrial fibrillation; myocardial infarction
类别
资金
- British Heart Foundation [TG/18/5/34111, PG/16/78/32402]
- Heart Research UK [RG2584]
- Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology)
- European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combatting Coronavirus Infections Award [H2020-JTI-IMI2 101005122]
- AI for Health Imaging Award CHAIMELEON: Accelerating the Lab to Market Transition of AI Tools for Cancer Management [H2020-SC1-FA-DTS-2019-1 952172]
- UK Research and Innovation [MR/V023799/1]
- UKRI [MR/V023799/1] Funding Source: UKRI
Segmentation of cardiac fibrosis and scars is critical for clinical diagnosis and treatment guidance, with advanced methods like deep learning showing more efficient and accurate results.
Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据