Multi-atlas segmentation and quantification of muscle, bone and subcutaneous adipose tissue in the lower leg using peripheral quantitative computed tomography.
Multi-atlas segmentation and quantification of muscle, bone and subcutaneous adipose tissue in the lower leg using peripheral quantitative computed tomography.
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DOI：
10.3389/fphys.2022.951368
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发表时间：
2022
影响因子：
4
通讯作者：
Ferrucci, Luigi
中科院分区：
文献类型：
--
作者：
Makrogiannis, Sokratis;Okorie, Azubuike;Di Iorio, Angelo;Bandinelli, Stefania;Ferrucci, Luigi
关键词：
准确和可重复的组织识别对于理解可能随衰老自然发生的结构和功能变化，或由于慢性疾病，或响应干预治疗至关重要。外周定量计算机断层扫描（pQCT）经常用于身体成分研究，特别是骨骼的结构和材料特性。此外，pQCT采集需要低辐射剂量，并且扫描仪紧凑且便携。然而，pQCT扫描具有有限的空间分辨率和中等SNR。pQCT图像质量经常由于图像采集期间受试者的无意识运动而降低。这些限制通常可能会损害组织量化的准确性，并强调需要自动化和鲁棒的量化方法。我们提出了一种组织识别和量化的方法，解决图像质量的限制和文物，在主题运动的兴趣增加。我们介绍了一个多图集图像分割（迈斯）框架的语义分割的硬组织和软组织的pQCT扫描在多个层次的小腿。我们描述了统计图谱生成，变形注册和多组织分类器融合的阶段。我们评估了我们的方法使用多种变形配准方法对参考组织掩模的性能。我们还评估了传统的基于模型的分割对相同的参考数据，以方便比较的性能。我们研究了受试者运动对组织分割质量的影响。我们还将性能最佳的方法应用于更大的样本外数据集并报告量化结果。结果表明，多图集图像分割与同构变形和概率标签融合产生非常好的质量在所有组织，即使扫描质量显着下降。将我们的技术应用于较大的数据集，揭示了与年龄相关的身体成分变化的趋势，与文献一致。由于其对受试者运动伪影的鲁棒性，我们的迈斯方法能够比传统的最先进的方法分析更大数量的扫描。pQCT中软组织和硬组织的自动分析是这项工作的另一个贡献。
Accurate and reproducible tissue identification is essential for understanding structural and functional changes that may occur naturally with aging, or because of a chronic disease, or in response to intervention therapies. Peripheral quantitative computed tomography (pQCT) is regularly employed for body composition studies, especially for the structural and material properties of the bone. Furthermore, pQCT acquisition requires low radiation dose and the scanner is compact and portable. However, pQCT scans have limited spatial resolution and moderate SNR. pQCT image quality is frequently degraded by involuntary subject movement during image acquisition. These limitations may often compromise the accuracy of tissue quantification, and emphasize the need for automated and robust quantification methods. We propose a tissue identification and quantification methodology that addresses image quality limitations and artifacts, with increased interest in subject movement. We introduce a multi-atlas image segmentation (MAIS) framework for semantic segmentation of hard and soft tissues in pQCT scans at multiple levels of the lower leg. We describe the stages of statistical atlas generation, deformable registration and multi-tissue classifier fusion. We evaluated the performance of our methodology using multiple deformable registration approaches against reference tissue masks. We also evaluated the performance of conventional model-based segmentation against the same reference data to facilitate comparisons. We studied the effect of subject movement on tissue segmentation quality. We also applied the top performing method to a larger out-of-sample dataset and report the quantification results. The results show that multi-atlas image segmentation with diffeomorphic deformation and probabilistic label fusion produces very good quality over all tissues, even for scans with significant quality degradation. The application of our technique to the larger dataset reveals trends of age-related body composition changes that are consistent with the literature. Because of its robustness to subject motion artifacts, our MAIS methodology enables analysis of larger number of scans than conventional state-of-the-art methods. Automated analysis of both soft and hard tissues in pQCT is another contribution of this work.
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影响因子：
5
作者：
Cordova, James S.;Schreibmann, Eduard;Holder, Chad A.
通讯作者：
Holder, Chad A.
DOI：
10.1111/j.1532-5415.2000.tb03873.x
发表时间：
2000-12-01
影响因子：
6.3
作者：
Ferrucci, L;Bandinelli, S;Guralnik, JM
通讯作者：
Guralnik, JM
影响因子：
10.9
作者：
Cardoso, M. Jorge;Leung, Kelvin;Ourselin, Sebastien
通讯作者：
Ourselin, Sebastien
在受试者运动的存在下对PQCT图像质量的标准化评估：定性与定量评估。
影响因子：
4.2
作者：
Blew, Robert M.;Lee, Vinson R.;Farr, Joshua N.;Schiferl, Daniel J.;Going, Scott B.
通讯作者：
Going, Scott B.
影响因子：
10.6
作者：
Mattes, D;Haynor, DR;Eubank, W
通讯作者：
Eubank, W