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基于深度学习的增强自动左骨细分方案与属性增强

Kamonchat Apivanichkul¹, Pattarapong Phasukkit^1,2, Pittaya Dankulchai³

¹School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.

Sensors (Basel, Switzerland)

|July 8, 2023

概括

增加计算机断层扫描 (CT) 切片与数据属性显著改善了深度学习,用于自动左股骨细分. 这种方法提高了医疗成像的细分精度和3D重建质量.

科学领域:

医疗成像医学成像
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

在计算机断层扫描 (CT) 扫描中,左腿骨的精确细分对于骨科分析和手术规划至关重要.
目前用于股骨细分的深度学习模型可能受到患者定位和数据质量变化的限制.

研究的目的:

为了提高基于深度学习的自动左腿骨细分方案的性能.
调查CT切片增强数据属性,特别是卧位,对细分精度的影响.

主要方法:

开发了一种深度学习模型,用于自动对左股骨进行细分.
根据数据增强策略,CT数据集被分为八个组 (F-I-F-VIII).
细分的性能被评估使用子相似系数 (DSC),交叉在联盟 (IoU),光谱角度映射器 (SAM) 和结构相似度指数 (SSIM).

主要成果:

用DSC (88.25%) 和IoU (80.85%) 来衡量,最高的细分性能是使用具有大特征系数 (类别F-IV) 的剪切和增强CT数据集实现的.
该模型显示了预测和基准真实3D重建之间的强烈相似性,SAM值在0.117-0.215之间,SSIM值在0.701-0.732.
医学图像预处理中的属性增强在改善细分结果方面被证明是有效的.

结论:

增加CT切片的数据属性,如躺着的位置,是增强基于深度学习的自动左股骨细分的新有效策略.
这种方法导致更准确的细分和可靠的3D重建,对临床应用有潜在的好处.
该研究强调了数据预处理技术在优化医疗图像分析深度学习模型性能方面的重要性.

关键词:

这就是U-Net.属性增强是一种属性增强.自动细分自动细分自动细分种植作物是一种作物.深度学习是一种深度学习.腿部骨头腿部骨头

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