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

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 nu...

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Veröffentlicht in:	International journal of legal medicine Jg. 133; H. 4; S. 1191 - 1205
Hauptverfasser:	Pröve, Paul-Louis, Jopp-van Well, Eilin, Stanczus, Ben, Morlock, Michael M., Herrmann, Jochen, Groth, Michael, Säring, Dennis, Auf der Mauer, Markus
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2019 Springer Nature B.V
Schlagworte:	3-D technology Adolescent Adolescents Adults Age Age Determination by Skeleton - methods Artificial neural networks Automation Bones Cartilage, Articular - diagnostic imaging Cartilage, Articular - pathology Chronology Datasets Documentation Encoders-Decoders Estimation Forensic Medicine Humans Image Processing, Computer-Assisted - methods Image segmentation Knee Knee Joint - diagnostic imaging Knee Joint - pathology Legal medicine Medical Law Medicine Medicine & Public Health Networks Neural networks Original Article Segmentation Teenagers Training Ultrasonic imaging Young Adult Young adults Knee Age estimation Segmentation Convolutional neural networks MRI
ISSN:	0937-9827, 1437-1596, 1437-1596
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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.
AbstractList	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.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. 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.
Author	Auf der Mauer, Markus Säring, Dennis Pröve, Paul-Louis Herrmann, Jochen Groth, Michael Stanczus, Ben Morlock, Michael M. Jopp-van Well, Eilin
Author_xml	– sequence: 1 givenname: Paul-Louis surname: Pröve fullname: Pröve, Paul-Louis organization: Department of Medical and Industrial Image Processing, University of Applied Sciences of Wedel – sequence: 2 givenname: Eilin surname: Jopp-van Well fullname: Jopp-van Well, Eilin organization: Department of Legal Medicine, University Medical Center Hamburg-Eppendorf (UKE) – sequence: 3 givenname: Ben surname: Stanczus fullname: Stanczus, Ben organization: Department of Medical and Industrial Image Processing, University of Applied Sciences of Wedel – sequence: 4 givenname: Michael M. surname: Morlock fullname: Morlock, Michael M. organization: Institute of Biomechanics M3, Hamburg University of Technology (TUHH) – sequence: 5 givenname: Jochen surname: Herrmann fullname: Herrmann, Jochen organization: Pediatric Radiology Department, University Medical Center Hamburg-Eppendorf (UKE) – sequence: 6 givenname: Michael surname: Groth fullname: Groth, Michael organization: Pediatric Radiology Department, University Medical Center Hamburg-Eppendorf (UKE) – sequence: 7 givenname: Dennis surname: Säring fullname: Säring, Dennis organization: Department of Medical and Industrial Image Processing, University of Applied Sciences of Wedel – sequence: 8 givenname: Markus orcidid: 0000-0002-5589-3681 surname: Auf der Mauer fullname: Auf der Mauer, Markus email: adm@fh-wedel.de organization: Department of Medical and Industrial Image Processing, University of Applied Sciences of Wedel
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Cites_doi	10.1109/3DV.2016.79 10.1109/CVPR.2016.90 10.1016/j.forsciint.2011.11.013 10.1007/s00330-017-4880-2 10.5244/C.30.124 10.1007/s00414-014-0987-z 10.1117/1.JMI.2.2.024001 10.1007/s00414-014-1020-2 10.1007/978-3-319-24574-4_28 10.1148/radiol.2243011259 10.1007/s12024-014-9559-2 10.1016/j.media.2017.07.005 10.1109/ISBI.2016.7493232 10.1109/TMI.2010.2046908 10.1007/s11517-011-0838-8 10.1007/978-3-642-54111-7_16 10.1016/j.compmedimag.2010.07.003 10.1073/pnas.1715832114 10.1007/s00194-010-0704-2 10.1007/s00414-014-0967-3 10.1007/978-3-319-46723-8_49 10.1007/s00194-010-0705-1 10.1016/j.forsciint.2016.10.002 10.1109/CVPR.2017.660 10.1007/s00414-018-1826-4 10.1016/j.forsciint.2015.12.006 10.1109/CVPR.2017.549 10.1109/72.572104 10.1007/978-3-319-10470-6_28
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Copyright	Springer-Verlag GmbH Germany, part of Springer Nature 2018 International Journal of Legal Medicine is a copyright of Springer, (2018). All Rights Reserved.
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Snippet	Age assessment is used to estimate the chronological age of an individual who lacks legal documentation. Recent studies indicate that the ossification degree...
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SubjectTerms	3-D technology Adolescent Adolescents Adults Age Age Determination by Skeleton - methods Artificial neural networks Automation Bones Cartilage, Articular - diagnostic imaging Cartilage, Articular - pathology Chronology Datasets Documentation Encoders-Decoders Estimation Forensic Medicine Humans Image Processing, Computer-Assisted - methods Image segmentation Knee Knee Joint - diagnostic imaging Knee Joint - pathology Legal medicine Medical Law Medicine Medicine & Public Health Networks Neural networks Original Article Segmentation Teenagers Training Ultrasonic imaging Young Adult Young adults
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Title	Automated segmentation of the knee for age assessment in 3D MR images using convolutional neural networks
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