Automated skeletal tissue quantification in the lower leg using peripheral quantitative computed tomography.
Automated skeletal tissue quantification in the lower leg using peripheral quantitative computed tomography.
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DOI：
10.1088/1361-6579/aaafb5
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发表时间：
2018-04-03
影响因子：
3.2
通讯作者：
Ferrucci L
中科院分区：
文献类型：
--
作者：
Makrogiannis S;Boukari F;Ferrucci L
在本文中，我们介绍一种使用外周定量计算机断层扫描（pQCT）在胫骨近端、中端和远端部位进行硬组织和软组织定量的方法。骨特性的定量对于评估骨结构对机械应力的抵抗力以及对负荷的适应性至关重要。可以计算软组织变量以研究肌肉体积和密度、肌肉 - 骨关系以及脂肪浸润。
我们采用隐式活动轮廓模型和聚类技术，在胫骨长度的4%、38%和66%处对骨、肌肉和脂肪进行自动分割和识别。接下来，我们计算每种组织类型的密度、面积和形状特征。我们将我们的方法实现为一个多平台工具，称为TIDAQ（组织识别与定量），供临床研究人员使用。
我们参照半自动工作流程获得的参考定量测量值和组织轮廓对所提出的方法进行了验证。测试方法与参考方法之间的戴明回归斜率平均值，对于横截面积为1.126，对于矿物质密度为1.078，表明一致性非常好。我们的方法得出了较高的平均变异系数（R²）估计值；在所有胫骨部位，横截面积为0.935，矿物质密度为0.888。此外，我们的组织分割方法在软组织和硬组织上取得了0.91的平均迪氏系数，表明轮廓描绘准确性非常高。
我们的方法应该能够实现对小腿肌肉和骨特性的高通量、准确且可重复的自动定量。这些信息对于评估未来不良后果的风险以及评估旨在提高骨和肌肉力量的药物、激素和行为干预的效果至关重要。
In this paper we introduce a methodology for hard and soft tissue quantification at proximal, intermediate and distal tibia sites using peripheral Quantitative Computed Tomography (pQCT) scans. Quantification of bone properties is crucial for estimating bone structure resistance to mechanical stress and adaptations to loading. Soft tissue variables can be computed to investigate muscle volume and density, muscle-bone relationship, and fat infiltration. We employed implicit active contour models and clustering techniques for automated segmentation and identification of bone, muscle and fat at 4%, 38%, and 66% tibia length. Next, we calculated densitometric, area and shape characteristics for each tissue type. We implemented our approach as a multi-platform tool denoted by TIDAQ (Tissue Identification and Quantification) to be used by clinical researchers. We validated the proposed method against reference quantification measurements and tissue delineations obtained by semi-automated workflows. The average Deming regression slope between the tested and reference method was 1.126 for cross-sectional areas and 1.078 for mineral densities indicating very good agreement. Our method produced high average coefficient of variation (R2) estimates; 0.935 for cross-sectional areas and 0.888 for mineral densities over all tibia sites. In addition, our tissue segmentation approach achieved an average Dice coefficient of 0.91 over soft and hard tissues indicating very good delineation accuracy. Our methodology should allow for high throughput, accurate and reproducible automatic quantification of muscle and bone characteristics of the lower leg. This information is critical to evaluate risk of future adverse outcomes and assess the effect of medications, hormones, and behavioral interventions aimed at improving bone and muscle strength.
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DOI：
10.1109/34.368173
发表时间：
1995-02-01
影响因子：
23.6
作者：
MALLADI, R;SETHIAN, JA;VEMURI, BC
通讯作者：
VEMURI, BC
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：
3.2
作者：
Prentice, A
通讯作者：
Prentice, A
影响因子：
2.5
作者：
Calder, Kristina M.;Inglis, Dean;MacIntyre, Norma J.
通讯作者：
MacIntyre, Norma J.
影响因子：
2.5
作者：
Lala, Deena;Cheung, Angela M.;Giangregorio, Lora
通讯作者：
Giangregorio, Lora