Skip to main content

Hand Pose Estimation for Pediatric Bone Age Assessment

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11769)

Abstract

We present a new experimental framework for the task of Bone Age Assessment (BAA) based on a local analysis of anatomical Regions Of Interest (ROIs) of hand radiographs. For this purpose, we introduce the Radiological Hand Pose Estimation (RHPE) Dataset, composed of 6,288 hand radiographs from a population that is different from the currently available BAA datasets. We provide Bone Age groundtruths annotated by two expert radiologists as well as bounding boxes and keypoints denoting anatomical ROIs annotated by multiple trained subjects. In addition to RHPE, we provide bounding boxes and ROIs annotations for the publicly available BAA dataset by the Radiological Society of North America (RSNA) [9]. We propose a new experimental framework with hand detection and hand pose estimation as new tasks to extract local information for BAA methods. Thanks to its fine-grained and precisely localized annotations, our dataset will allow to exploit local information to push forward automated BAA algorithms. Additionally, we conduct experiments with state-of-the-art methods in each of the new tasks. Our proposed model, named BoNet, leverages local information and significantly outperforms state-of-the-art methods in BAA. We provide the RHPE dataset with the corresponding annotations, as well as the trained models, the source code for BoNet and the additional annotations created for the RSNA dataset.

Keywords

  • Bone Age Assessment
  • Computer aided diagnosis
  • Hand radiograph
  • Regions Of Interest

M. Escobar and C. González—Both authors contributed equally to this work.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-32226-7_59
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-32226-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.

Notes

  1. 1.

    https://biomedicalcomputervision.uniandes.edu.co/.

References

  1. Cicero, M., Bilbily, A.: Machine learning and the future of radiology: how we won the 2017 RSNA ML challenge (2017)

    Google Scholar 

  2. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 23, 681–685 (2001). https://doi.org/10.1109/34.927467

    CrossRef  Google Scholar 

  3. Gaskin, C.M., Kahn, M.M.S.L., Bertozzi, J.C., Bunch, P.M.: Skeletal Development of the Hand and Wrist: A Radiographic Atlas and Digital Bone Age Companion. Oxford University Press, Oxford (2011)

    CrossRef  Google Scholar 

  4. Ge, L., Cai, Y., Weng, J., Yuan, J.: Hand PointNet: 3D hand pose estimation using point sets. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8417–8426 (2018)

    Google Scholar 

  5. Ge, L., Ren, Z., Yuan, J.: Point-to-point regression pointnet for 3D hand pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 489–505. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_29

    CrossRef  Google Scholar 

  6. Gertych, A., Zhang, A., Sayre, J., Pospiech-Kurkowska, S., Huang, H.: Bone age assessment of children using a digital hand atlas. Comput. Med. Imaging Graph. 31(4–5), 322–331 (2007)

    CrossRef  Google Scholar 

  7. Gilsanz, V., Ratib, O.: Hand Bone Age: A Digital Atlas of Skeletal Maturity. Springer, Heidelberg (2005)

    Google Scholar 

  8. Greulich, W.W., Pyle, S.I., Todd, T.W.: Radiographic Atlas of Skeletal Development of the Hand and Wrist, vol. 2. Stanford University Press, Palo Alto (1959)

    Google Scholar 

  9. Halabi, S.S., Prevedello, L.M., et al.: The RSNA pediatric bone age machine learning challenge. Radiology 290(2), 498–503 (2019)

    CrossRef  Google Scholar 

  10. Hardyck, C., Petrinovich, L.F.: Left-handedness. Psychol. Bull. 84(3), 385 (1977)

    CrossRef  Google Scholar 

  11. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  12. Iglovikov, V.I., Rakhlin, A., Kalinin, A.A., Shvets, A.A.: Paediatric bone age assessment using deep convolutional neural networks. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 300–308. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_34

    CrossRef  Google Scholar 

  13. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    CrossRef  Google Scholar 

  14. Massa, F., Girshick, R.: maskrcnn-benchmark: fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch (2018)

    Google Scholar 

  15. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 91–99 (2015)

    Google Scholar 

  16. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27–30 June 2016, pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308

  17. Tanner, J., Whitehouse, R., Marshall, W., Carter, B.: Prediction of adult height from height, bone age, and occurrence of menarche, at ages 4 to 16 with allowance for midparent height. Arch. Dis. Child. 50(1), 14–26 (1975)

    CrossRef  Google Scholar 

  18. Thodberg, H., Kreiborg, S., Juul, A., Pedersen, K.: The BoneXpert method for automated determination of skeletal maturity. IEEE Trans. Med. Imaging 28(1), 52–66 (2009). https://doi.org/10.1109/tmi.2008.926067

    CrossRef  Google Scholar 

  19. Xiao, B., Wu, H., Wei, Y.: Simple Baselines for Human Pose Estimation and Tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 472–487. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_29

    CrossRef  Google Scholar 

Download references

Acknowledgments

This project was partially funded by Colciencias grant 841-2017. The authors thank Edgar Margffoy-Tuay for his support in developing the annotation server and the students of IBIO-3470 at Uniandes for their help as annotators.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to María Escobar .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 2 (mp4 14995 KB)

Supplementary material 1 (pdf 591 KB)

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Escobar, M., González, C., Torres, F., Daza, L., Triana, G., Arbeláez, P. (2019). Hand Pose Estimation for Pediatric Bone Age Assessment. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_59

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-32226-7_59

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32225-0

  • Online ISBN: 978-3-030-32226-7

  • eBook Packages: Computer ScienceComputer Science (R0)