| 周雨,钟维佳,黄天星,李文洁,周治明.平扫CT深度学习用于自发性脑出血研究进展[J].中国医学影像技术,2024,40(12):1945~1948 |
| 平扫CT深度学习用于自发性脑出血研究进展 |
| Research progresses of deep learning based on non-contrast CT in spontaneous intracerebral hemorrhage |
| 投稿时间：2024-05-28 修订日期：2024-07-09 |
| DOI：10.13929/j.issn.1003-3289.2024.12.029 |
| 中文关键词: 脑出血 血肿 体层摄影术,X线计算机 深度学习 |
| 英文关键词:cerebral hemorrhage hematoma tomography, X-ray computed deep learning |
| 基金项目:重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX0116)、重庆市科卫联合项目(2025MSXM009)。 |
|
| 摘要点击次数: 330 |
| 全文下载次数: 92 |
| 中文摘要: |
| 自发性脑出血(sICH)为发生于脑实质内的非外伤性血管破裂出血,是致残率及致死率均较高的神经系统急症,早期诊断和治疗对改善预后至关重要。非对比增强CT(NCT)是诊断脑出血的主要影像学方法。近年来,深度学习(DL)已在自动检测脑出血、分割并计算血肿体积及识别血肿周围水肿等方面展现出巨大潜力,可辅助医师诊疗以降低sICH死亡率、改善患者生活质量。本文就NCT DL用于sICH研究进展进行综述。 |
| 英文摘要: |
| Spontaneous intracerebral hemorrhage (sICH) refers to non-traumatic bleeding within brain parenchyma, presenting as a neurological emergency with high disability rate and mortality. Early diagnosis and treatment sICH are important to improve prognosis. Non-contrast CT (NCT) is a primary imaging modality for diagnosing intracerebral hemorrhage. In recent years, deep learning (DL) had shown unparalleled potential in automatic detection of cerebral hemorrhage, segmentation and calculation of hematoma volume, identification of edema around hematoma, etc, being able to assist doctors in diagnosis and treatment of sICH for reducing mortality and improving patients' life quality. The research progresses of DL based on NCT in sICH were reviewed in this article. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
