Rucci M, Coppini G, Nicoletti I, Cheli D, Valli G
Scuola Superiore S. Anna, Pisa, Italy.
Comput Biomed Res. 1995 Jun;28(3):239-56. doi: 10.1006/cbmr.1995.1016.
The assessment of skeletal maturity is crucial for the analysis of growth disorders and plays an important role in paediatrics. For this reason, several methods have been developed for estimating skeletal maturity. Among them, the Tanner and Whitehouse method (TW2), which is based on the analysis of hand radiographs, is usually considered the most accurate and reliable. Nevertheless, TW2 is applied only in a small fraction of cases, due to its complexity and long examination times. Thus, the development of automated systems which reliably implement this method is highly desirable. However, major difficulties have been found in the development of computer-based systems for the assessment of skeletal maturity. In particular the extraction of the bones of interest has proved to be extremely challenging. In this paper, we propose a system architecture for the implementation of the TW2 method, which is based on artificial neural networks. For each bone considered, the maturation stage is determined by means of a two-step process which first locates the position of the bone in the radiograph and then analyzes the bone shape. Experimental results obtained with our implementation of the carpal version of TW2 are in good agreement with those provided by trained observers.
骨骼成熟度的评估对于生长障碍的分析至关重要，并且在儿科学中发挥着重要作用。因此，已经开发了几种用于估计骨骼成熟度的方法。其中，基于手部X光片分析的坦纳和怀特豪斯方法（TW2）通常被认为是最准确和可靠的。然而，由于其复杂性和检查时间长，TW2仅在一小部分病例中应用。因此，非常需要开发能够可靠实施该方法的自动化系统。然而，在开发用于评估骨骼成熟度的基于计算机的系统时发现了重大困难。特别是，感兴趣骨骼的提取已被证明极具挑战性。在本文中，我们提出了一种基于人工神经网络的用于实施TW2方法的系统架构。对于所考虑的每根骨骼，成熟阶段通过两步过程确定，该过程首先在X光片中定位骨骼的位置，然后分析骨骼形状。我们对TW2腕骨版本的实现所获得的实验结果与训练有素的观察者提供的结果高度一致。
