Hand Pose Estimation for Pediatric Bone Age Assessment

  • María EscobarEmail author
  • Cristina González
  • Felipe Torres
  • Laura Daza
  • Gustavo Triana
  • Pablo Arbeláez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)


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.


Bone Age Assessment Computer aided diagnosis Hand radiograph Regions Of Interest 



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.

Supplementary material

490281_1_En_59_MOESM1_ESM.pdf (592 kb)
Supplementary material 1 (pdf 591 KB)

Supplementary material 2 (mp4 14995 KB)


  1. 1.
    Cicero, M., Bilbily, A.: Machine learning and the future of radiology: how we won the 2017 RSNA ML challenge (2017)Google Scholar
  2. 2.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 23, 681–685 (2001). CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 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). Scholar
  6. 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)CrossRefGoogle Scholar
  7. 7.
    Gilsanz, V., Ratib, O.: Hand Bone Age: A Digital Atlas of Skeletal Maturity. Springer, Heidelberg (2005)Google Scholar
  8. 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. 9.
    Halabi, S.S., Prevedello, L.M., et al.: The RSNA pediatric bone age machine learning challenge. Radiology 290(2), 498–503 (2019)CrossRefGoogle Scholar
  10. 10.
    Hardyck, C., Petrinovich, L.F.: Left-handedness. Psychol. Bull. 84(3), 385 (1977)CrossRefGoogle Scholar
  11. 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. 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). Scholar
  13. 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). Scholar
  14. 14.
    Massa, F., Girshick, R.: maskrcnn-benchmark: fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch (2018)Google Scholar
  15. 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. 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).
  17. 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)CrossRefGoogle Scholar
  18. 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). Scholar
  19. 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). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • María Escobar
    • 1
    Email author
  • Cristina González
    • 1
  • Felipe Torres
    • 1
  • Laura Daza
    • 1
  • Gustavo Triana
    • 2
  • Pablo Arbeláez
    • 1
  1. 1.Universidad de los AndesBogotáColombia
  2. 2.Fundación Santa Fe de BogotáBogotáColombia

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