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Automated segmentation of the knee for age assessment in 3D MR images using convolutional neural networks

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Abstract

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.

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Funding

This project is funded by the German Research Foundation (DFG), Project (SA 2530/6-1) and (JO 1198/2-1).

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Authors and Affiliations

  1. Department of Medical and Industrial Image Processing, University of Applied Sciences of Wedel, Feldstraße 143, 22880, Wedel, Germany

    Paul-Louis Pröve, Ben Stanczus, Dennis Säring & Markus Auf der Mauer

  2. Department of Legal Medicine, University Medical Center Hamburg-Eppendorf (UKE), Butenfeld 34, 22529, Hamburg, Germany

    Eilin Jopp-van Well

  3. Institute of Biomechanics M3, Hamburg University of Technology (TUHH), Denickestraße 15, 21073, Hamburg, Germany

    Michael M. Morlock

  4. Pediatric Radiology Department, University Medical Center Hamburg-Eppendorf (UKE), Martinistr. 52, 20246, Hamburg, Germany

    Jochen Herrmann & Michael Groth

Authors
  1. Paul-Louis Pröve
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  2. Eilin Jopp-van Well
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  3. Ben Stanczus
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  4. Michael M. Morlock
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  5. Jochen Herrmann
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  6. Michael Groth
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  7. Dennis Säring
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  8. Markus Auf der Mauer
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Corresponding author

Correspondence to Markus Auf der Mauer.

Ethics declarations

An ethical approval for this study was granted by the Ethics Committee of the Medical Association Hamburg (PV4527). The director of this study had full control of the data and the material submitted for publication.

Conflict of interests

The authors declare that they have no conflict of interest.

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Written informed consent was obtained from all subjects in this study.

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Pröve, PL., Jopp-van Well, E., Stanczus, B. et al. Automated segmentation of the knee for age assessment in 3D MR images using convolutional neural networks. Int J Legal Med 133, 1191–1205 (2019). https://doi.org/10.1007/s00414-018-1953-y

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  • DOI: https://doi.org/10.1007/s00414-018-1953-y

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