脑转移的自我细分 放射性外科手术今天在哪里?
Matthew Kim1, Jen-Yeu Wang1, Weiguo Lu2
1Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
Bioengineering (Basel, Switzerland)
|May 25, 2024
概括
使用深度学习 (DL) 的自动脑转移 (BM) 分段增强了诊断和治疗计划. 本综述分析了DL策略,以有效和安全的BM管理,改善患者护理.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射性外科手术 放射性外科
背景情况:
- 脑转移的检测和细分对于患者的管理至关重要.
- 随着肌肉瘤患病率的增加,需要自动化解决方案来提高效率和安全性.
研究的目的:
- 对大脑转移的深度学习 (DL) 自动细分策略进行审查和分析.
- 描述DL模型中使用的数据,并评估当前方法的性能.
- 讨论BM细分中的挑战和实施见解.
主要方法:
- 对医学图像细分深度学习近期进展的文献综述.
- 分析用于BM检测的自动细分策略,数据集和性能指标.
- 评估临床实施经验和挑战.
主要成果:
- 深度学习模型在医学图像细分方面取得了最先进的结果.
- 自动化细分可显著减少手工工作量,提高工作流效率.
- 各种DL策略显示出准确可靠的BM细分的希望.
结论:
- 深度学习为自动脑转移细分提供了强大的工具.
- 解决当前的挑战可以进一步提高这些方法的临床实用性.
- 优化细分可以改善治疗计划和患者在立体射线外科手术的结果.
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