Publisher DOI: 10.1007/s00414-018-1953-y
Title: Automated segmentation of the knee for age assessment in 3D MR images using convolutional neural networks
Language: English
Authors: Pröve, Paul Louis 
Jopp-van Well, Eilin 
Stanczus, Ben 
Morlock, Michael 
Herrmann, Jochen 
Groth, Michael 
Säring, Dennis 
Maur, Matthias auf der 
Issue Date: 1-Jul-2019
Source: International Journal of Legal Medicine 4 (133): 1191-1205 (2019-07-01)
Journal or Series Name: International journal of legal medicine 
Abstract (english): Age assessment is used to estimate the chronological age of an individual who lacks legal documentation. Recent studies indicate that the ossification degree of the growth plates in the knee joint correlates with chronological age of adolescents and young adults. To verify this hypothesis, a high number of datasets need to be analysed. An approach which enables an automated detection and analysis of the bone structures may be necessary to handle large datasets. The purpose of this study was to develop a fully automatic 2D knee segmentation based on 3D MR images using convolutional neural networks. A total of 76 datasets were available and divided into a training set (74%), a validation set (13%) and a test set (13%). Multiple preprocessing steps were applied to correct image intensity values and to reduce the image size. Image augmentation was employed to virtually increase the dataset size for training. The proposed architecture for the segmentation task resembles the encoder-decoder model type used for the U-Net. The trained network achieved a dice similarity coefficient score of 98% compared to the manual segmentations and an intersection over union of 96%. The precision and recall of the model were balanced, and the error was only 1.2%. No overfitting was observed during training. As a proof of concept, the predicted segmentations were used for the age estimation of 145 subjects. Initial results show the potential of this approach attaining a mean absolute error of 0.48 ± 0.32 years for a test set of 14 subjects. The proposed automated segmentation can contribute to faster, reproducible and potentially more reliable age estimation in the future.
ISSN: 0937-9827
Institute: Biomechanik M-3 
Type: (wissenschaftlicher) Artikel
Funded by: DFG Projekte JO 1198/2-1 und SA 2530/6-1
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