Urschler Martin, Grassegger Sabine, Štern Darko
a Ludwig Boltzmann Institute for Clinical Forensic Imaging , Graz , Austria .
b Institute for Computer Graphics and Vision, Graz University of Technology, BioTechMed , Graz , Austria , and.
Ann Hum Biol. 2015;42(4):358-67. doi: 10.3109/03014460.2015.1043945. Epub 2015 Aug 27.
Age estimation of individuals is important in human biology and has various medical and forensic applications. Recent interest in MR-based methods aims to investigate alternatives for established methods involving ionising radiation. Automatic, software-based methods additionally promise improved estimation objectivity.
To investigate how informative automatically selected image features are regarding their ability to discriminate age, by exploring a recently proposed software-based age estimation method for MR images of the left hand and wrist.
One hundred and two MR datasets of left hand images are used to evaluate age estimation performance, consisting of bone and epiphyseal gap volume localisation, computation of one age regression model per bone mapping image features to age and fusion of individual bone age predictions to a final age estimate.
Quantitative results of the software-based method show an age estimation performance with a mean absolute difference of 0.85 years (SD = 0.58 years) to chronological age, as determined by a cross-validation experiment. Qualitatively, it is demonstrated how feature selection works and which image features of skeletal maturation are automatically chosen to model the non-linear regression function.
Feasibility of automatic age estimation based on MRI data is shown and selected image features are found to be informative for describing anatomical changes during physical maturation in male adolescents.
个体年龄估计在人类生物学中很重要，并且有各种医学和法医应用。最近对基于磁共振成像（MR）的方法的关注旨在探索替代涉及电离辐射的既定方法。基于软件的自动方法还有望提高估计的客观性。
通过探索一种最近提出的基于软件的左手和手腕MR图像年龄估计方法，研究自动选择的图像特征在区分年龄能力方面的信息量。
使用102个左手图像的MR数据集来评估年龄估计性能，包括骨骼和骨骺间隙体积定位、为每个骨骼映射图像特征计算一个年龄回归模型以预测年龄，以及将各个骨骼年龄预测融合为最终年龄估计。
基于软件的方法的定量结果显示，通过交叉验证实验确定，与实际年龄相比，年龄估计性能的平均绝对差异为0.85岁（标准差=0.58岁）。定性地展示了特征选择是如何工作的，以及哪些骨骼成熟的图像特征被自动选择来建模非线性回归函数。
展示了基于MRI数据进行自动年龄估计的可行性，并且发现所选图像特征对于描述男性青少年身体成熟过程中的解剖学变化具有信息量。
