开源域调整以处理数据转移用于体积细分-用例细分脏细分的数据转移.
Ramon Correa-Medero1, Umar Ghaffar2, Sam Fathizadeh3
1School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.
European radiology
|June 28, 2025
概括
这项研究引入了一种新的域适应方法,用于CT扫描中强大的脏细分,提高不同对比相和脏状况的准确性. 开源模型确保可靠的体积测量,以更好地诊断和治疗患者.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 目前的脏细分模型因CT扫描对比相的变化而面临性能下降.
- 对比吸收和脏异常的差异对准确的脏体积评估具有挑战.
研究的目的:
- 开发一种域适应方法,从CT体积中进行强大的脏细分,独立于对比剂量和功能.
- 解决领域转移,包括对比到非对比,动脉到静脉阶段,以及正常到异常的脏.
主要方法:
- 使用隐性空间分辨器的域适应技术被采用.
- 该模型在公开的非对比和动脉相数据集上进行了训练.
- 验证是在不同的公共和私人数据集上进行的,具有不同的对比度阶段和脏异常.
主要成果:
- 拟议的模型在四个数据集中实现了0.8892 DICE分数,其中包括域移位.
- 它在外部验证方面表现优于TotalSegmentator等基线模型和其他域调整方法.
- 该方法证明了细分质量的提高,并且需要比基线少的数据.
结论:
- 域调整显著提高了CT扫描中的脏细分质量.
- 开发的模型提供了准确的体积测量,无论对比度变化或异常.
- 一个开源代码库可用,促进临床相关性和更广泛的患者护理采用.
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