从使用二维和三维深度学习模型的微型CT图像中对皮层骨,脊椎骨和髓孔的细分
Andrew H Lee1,2,3, Julian M Moore3, Brandon Vera Covarrubias1
1Department of Anatomy, College of Graduate Studies, Midwestern University, Glendale, Arizona, USA.
Anatomical record (Hoboken, N.J. : 2007)
|February 5, 2025
概括
卷积神经网络 (CNN) 从CT扫描中自动化骨细分. 虽然2D模型在细节方面略高于3D模型,但2D CNNs被推用于骨图像分析的效率.
科学领域:
- 生物医学工程 生物医学工程
- 医疗成像医学成像
- 计算生物学 计算生物学
背景情况:
- 计算机断层扫描 (CT) 提供了快速的骨成像,但需要耗时的手动细分.
- 手动细分容易出现错误,需要自动化解决方案.
- 卷积神经网络 (CNN) 显示出对自动图像分析的前景.
研究的目的:
- 开发和评估基于CNN的模型,用于自动化骨分割.
- 为了比较2D和3DCNN模型的性能,用于微型CT骨细分.
- 评估BONe (骨一次性网络) 模型的效率和准确性.
主要方法:
- 从头开始训练CNN细分模型,使用来自长骨的微型CT扫描的2D和3D补丁.
- 开发了BONe (骨一次性网络) 模型,用于自动骨细分.
- 对比了2D和3D CNN架构的性能.
主要成果:
- 2D CNN模型在细分精细细节方面略高于3D模型,例如状骨.
- 3D CNN模型表现出更好的概括性,并产生更光滑的内部骨表面.
- 与2D模型相比,3D模型的巨大计算成本限制了它们的实用性.
结论:
- 由于其效率和准确性,推使用二维CNN模型进行骨分割.
- BONe模型显示了在不同物种和扫描品质的骨图像中更广泛应用的潜力.
- 对BONe模型的进一步改进可能会将它们的实用性扩展到骨细分和其他解剖结构.
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