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超声图像中跟腱平均厚度的计算机辅助定量分析:肌腱病的影响

Computer-based quantification of the mean Achilles tendon thickness in ultrasound images: effect of tendinosis.

作者信息

Syha R, Peters M, Birnesser H, Niess A, Hirschmueller A, Dickhuth H-H, Sandrock M

机构信息

Freiburg University Hospital, Centre for Internal Medicine, Department for Rehabilitative and Preventative Sports Medicine, Freiburg, Germany.

出版信息

Br J Sports Med. 2007 Dec;41(12):897-902; discussion 902. doi: 10.1136/bjsm.2007.037812. Epub 2007 Jun 5.

Abstract

BACKGROUND

B-mode measurement of the sagital diameter of the Achilles tendon based on a manual tracing (MT) procedure is partly dependent on the subjectivity of the reader. The aim of this study is to establish a standardised automatic procedure to differentiate between normal and chronically degenerated tendons. For this comparison, the tracing results of the tendon boundaries of an automatic identification (AI) process, already established with the detection of intima-media thickness, are compared with computer-assisted MT.

METHODS

The detection of the tendon boundaries was performed in 115 ultrasound images including the cranial border of the calcaneal tuberosity. The measured section (starting point 4 cm away from the anterior boundary of the calcaneal tuberosity) amounted to 3 cm, and was divided into three sub-segments (1 cm each). Intra- and inter-reader/observer variability for mean and maximum Achilles tendon thickness (ATT) with AI and MT were evaluated. A normal group and a group with clinically diagnosed chronic tendon degeneration had mean and maximum ATT readings compared.

RESULTS

Using MT, the intra- and inter-reader variability was 3.0% and 6.8%, respectively, using AI the variability was 1.6% and 3.9%, respectively. Mean and maximum ATT were measured systematically lower by AI compared to MT in all regions by 0.4 mm. The AI procedure was most accurate in the second segment. The mean ATT and maximum ATT were correctly detected in 93.9% and 96.6% of the images.

CONCLUSION

The AI procedure detected the ATT with a high level of precision in all three segments. The most robust measurement was reached in the second segment. It eliminates most of the inter-/intra-reader variability in ATT measurement using MT. We suggest this new method could be a new gold standard for quantification of chronic disorder in Achilles tendons.

摘要

背景

基于手动追踪(MT)程序的B超测量跟腱矢状径部分取决于读者的主观性。本研究的目的是建立一种标准化的自动程序,以区分正常和慢性退变的肌腱。为了进行这种比较,将已经用于检测内膜中层厚度的自动识别(AI)过程中肌腱边界的追踪结果与计算机辅助MT进行比较。

方法

在115幅包括跟骨结节颅侧边界的超声图像中进行肌腱边界检测。测量段(距跟骨结节前边界4 cm处为起点)为3 cm,分为三个子段(各1 cm)。评估了AI和MT测量跟腱平均厚度(ATT)和最大厚度时的读者内和读者间/观察者间变异性。比较了正常组和临床诊断为慢性肌腱退变组的ATT平均读数和最大读数。

结果

使用MT时,读者内和读者间变异性分别为3.0%和6.8%,使用AI时变异性分别为1.6%和3.9%。与MT相比,AI在所有区域系统测量的ATT平均和最大厚度均低0.4 mm。AI程序在第二段最准确。在93.9%和96.6%的图像中正确检测到ATT平均厚度和最大厚度。

结论

AI程序在所有三个段中都能高精度地检测ATT。在第二段测量最稳定。它消除了使用MT测量ATT时的大部分读者间/读者内变异性。我们认为这种新方法可能成为量化跟腱慢性疾病的新金标准。

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