Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Japan.
Graduate School of Medicine, Osaka University, Suita, Japan.
Int J Comput Assist Radiol Surg. 2018 Jul;13(7):977-986. doi: 10.1007/s11548-018-1758-y. Epub 2018 Apr 6.
Patient-specific quantitative assessments of muscle mass and biomechanical musculoskeletal simulations require segmentation of the muscles from medical images. The objective of this work is to automate muscle segmentation from CT data of the hip and thigh.
We propose a hierarchical multi-atlas method in which each hierarchy includes spatial normalization using simpler pre-segmented structures in order to reduce the inter-patient variability of more complex target structures.
The proposed hierarchical method was evaluated with 19 muscles from 20 CT images of the hip and thigh using the manual segmentation by expert orthopedic surgeons as ground truth. The average symmetric surface distance was significantly reduced in the proposed method (1.53 mm) in comparison with the conventional method (2.65 mm).
We demonstrated that the proposed hierarchical multi-atlas method improved the accuracy of muscle segmentation from CT images, in which large inter-patient variability and insufficient contrast were involved.
患者特异性肌肉质量定量评估和生物力学肌肉骨骼模拟需要从医学图像中对肌肉进行分割。本研究的目的是实现从髋关节和大腿 CT 数据中自动分割肌肉。
我们提出了一种分层多图谱方法，其中每个层次都包括使用更简单的预分割结构进行空间归一化，以减少更复杂目标结构的患者间变异性。
使用由专家骨科医生进行的手动分割作为金标准，对来自髋关节和大腿的 20 个 CT 图像的 19 块肌肉进行了所提出的分层方法的评估。与传统方法（2.65mm）相比，所提出方法的平均对称面距离（1.53mm）显著降低。
我们证明了所提出的分层多图谱方法提高了涉及大的患者间变异性和不足的对比度的 CT 图像中肌肉分割的准确性。
