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通过转移学习从CT图像进行加速肌肉质量估计

Seunghan Yoon¹, Tae Hyung Kim², Young Kul Jung³

¹Department of Computer Science & Engineering (Major in Bio Artificial Intelligence), Hanyang University at Ansan, 55, Hanyangdaehak-ro, Sangnok-gu, 15588, Ansan-si, Gyeonggi-do, Republic of Korea.

BMC medical imaging

|October 9, 2024

概括

使用VNet模型的转移学习显著提高了使用有限数据的医疗图像细分精度. 这种方法提高了对CT成像深度学习模型的设备变化和灾难性遗忘的稳定性.

科学领域:

医疗成像医学成像
深度学习 (Deep Learning) 是一种深度学习.
计算机视觉计算机视觉

背景情况:

医疗图像细分的深度学习面临着高的数据标签成本.
计算机断层扫描 (CT) 设备的变化阻碍了模型的概括性.

研究的目的:

开发一种高效的深度学习策略,用于使用CT扫描进行医学图像细分.
为了应对不同CT设备的细分精度和模型稳定性的挑战.

主要方法:

建议使用SEED (小型,手动标记的) 图像进行转移学习,以训练VNet细分模型.
根据UNet,UNETR和Swin-UNETR模型对VNet进行评估.
在多设备训练场景中,由于灾难性遗忘而导致的评估模型性能恶化.

主要成果:

转移学习有效地训练了使用最小数据的细分模型.
与其他模型和半自动化工具相比,VNet在肌肉和肝脏细分方面表现出卓越的表现.
对于灾难性遗忘,VNet表现出了最高的稳定性.

结论:

基于CNN的网络,特别是VNet,优于基于变压器的网络和用于二维CT图像分割的半自动工具.
拟议的战略提高了医疗图像细分的效率和稳定性.

关键词:

CT图像细分的部分化CT图像细分卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.医疗图像细分医疗图像细分

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