Bone Age assessment is a critical factor for determining delayed development in children, which can be a sign of pathologies such as endocrine diseases, growth abnormalities, chromosomal, neurological and congenital disorders among others. Typically, specialists such as pediatric endocrinologists inspect visually an X-ray of the non-dominant hand of the child, like the ones shown in the Figure, and compare them against examples from textbook atlases to estimate bone age and predict adult height.
In collaboration with the Radiology Department at Fundación Santa Fe de Bogotá and funded by Colciencias, we are developing automated approaches to support specialists in the assessment of skeletal maturity in pediatric patients in Colombia.

Bone Age Assessment Resources

We created the Bone Age Assessment Resources (BAAR) as a platform for promoting the development of BAA algorithms. In the BAAR you can download the RSNA and RHPE datasets with keypoints, bounding box and boneage annotations for the training and validation sets. Additionally, you can explore an overview of the methods BCV has developed for this task. Finally, the BAAR include an evaluation server for the test set of RHPE and RSNA.

Publications

SIMBA: Specific Identity Markers for Bone Age Assessment

C. González*, M. Escobar*, L. Daza, F. Torres, G Triana and P. Arbeláez

23rd International Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020

Hand Pose Estimation for Pediatric Bone Age Assessment

M. Escobar*, C. González*, F. Torres, L. Daza, G Triana and P. Arbeláez

22nd International Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019

An Empirical Study on Global Bone Age Assessment

F. Torres, C. González, M. Escobar, L. Daza, G Triana and P. Arbeláez

15th International Conference on Medical Information Processing and Analysis (SIPAIM), 2019

Bone age detection via carpogram analysis using convolutional neural networks

F. Torres, M.A. Bravo, E Salinas, G Triana and P. Arbeláez

13th International Conference on Medical Information Processing and Analysis (SIPAIM), 2017

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