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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 pp 531–539Cite as

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.

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Notes

  1. 1.

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

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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

  1. Universidad de los Andes, Bogotá, Colombia

    María Escobar, Cristina González, Felipe Torres, Laura Daza & Pablo Arbeláez

  2. Fundación Santa Fe de Bogotá, Bogotá, Colombia

    Gustavo Triana

Authors
  1. María Escobar
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  2. Cristina González
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  3. Felipe Torres
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  4. Laura Daza
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  5. Gustavo Triana
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  6. Pablo Arbeláez
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Corresponding author

Correspondence to María Escobar .

Editor information

Editors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Dinggang Shen

  2. University of Georgia, Athens, GA, USA

    Prof. Tianming Liu

  3. Western University, London, ON, Canada

    Dr. Terry M. Peters

  4. Yale University, New Haven, CT, USA

    Lawrence H. Staib

  5. University of Strasbourg, Illkirch, France

    Caroline Essert

  6. United Imaging Intelligence, Shanghai, China

    Sean Zhou

  7. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Prof. Dr. Pew-Thian Yap

  8. Western University, London, ON, Canada

    Ali Khan

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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

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

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