Bone age detection via carpogram analysis using convolutional neural networks

Felipe Torres; María Alejandra Bravo; Emmanuel Salinas; Gustavo Triana; Pablo Arbeláez

doi:10.1117/12.2285949

17 November 2017 Bone age detection via carpogram analysis using convolutional neural networks

Felipe Torres, María Alejandra Bravo, Emmanuel Salinas, Gustavo Triana, Pablo Arbeláez

Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 1057217 (2017) https://doi.org/10.1117/12.2285949
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia

Abstract

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. In this paper we present BoneNet, a methodology to assess automatically the skeletal maturity state in pediatric patients based on Convolutional Neural Networks. We train and evaluate our algorithm on a database of X-Ray images provided by the hospital Fundacion Santa Fe de Bogot ´ a with around 1500 images of patients between the ages 1 to 18. ´ We compare two different architectures to classify the given data in order to explore the generality of our method. To accomplish this, we define multiple binary age assessment problems, dividing the data by bone age and differentiating the patients by their gender. Thus, exploring several parameters, we develop BoneNet. Our approach is holistic, efficient, and modular, since it is possible for the specialists to use all the networks combined to determine how is the skeletal maturity of a patient. BoneNet achieves over 90% accuracy for most of the critical age thresholds, when differentiating the images between over or under a given age.

Citation Download Citation

Felipe Torres, María Alejandra Bravo, Emmanuel Salinas, Gustavo Triana, and Pablo Arbeláez "Bone age detection via carpogram analysis using convolutional neural networks", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057217 (17 November 2017); https://doi.org/10.1117/12.2285949

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