Mr. Felipe Torres Figueroa Profile

Felipe Torres Figueroa

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Proceedings Article | 3 January 2020 Paper

An empirical study on global bone age assessment

Felipe Torres, Cristina González, María Camila Escobar, Laura Daza, Gustavo Triana, Pablo Arbeláez

Proceedings Volume 11330, 113300E (2020) https://doi.org/10.1117/12.2542431

KEYWORDS: Bone, X-ray imaging, X-rays, Machine learning, Convolutional neural networks, Network architectures

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Bone Age Assessment (BAA) is a task performed by physicians to estimate the skeletal development of a pediatric patient. Tipically physicians perform this exam by doing a manual analysis of the X-ray image of the non-dominant hand of a child, either by taking the image as a whole or paying attention to certain anatomical Regions Of Interest (ROIs). Over the years, several datasets have been proposed in order to generate automated methods to perform this task. Most notably, in 2017 the Radiological Society of North America (RSNA)1 created the Pediatric Bone Age Challenge, which encouraged the development of machine learning approaches for this task. In this paper, we present GPNet a convolutional neural network capable of performing BAA precisely and effectively by analyzing the whole hand in a single forward pass. We train GPNet using the training data available from the dataset created in the RSNA challenge and evaluate our method using the validation set. We use the testing set to compare our performance with the current state-of-the-art and find that GPNet significantly outperforms previous methods. During our architecture search we perform several experiments to demonstrate the effect of different layers, proving that some blocks do not contribute to the performance of the network, but instead they affect it. As a result, we are able to develop a method that reduces the number of trainable parameters by nearly 82.15 M in comparison to the state-of-the-art, while improving the performance.

Proceedings Article | 17 November 2017 Paper

Bone age detection via carpogram analysis using convolutional neural networks

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

Proceedings Volume 10572, 1057217 (2017) https://doi.org/10.1117/12.2285949

KEYWORDS: Bone, Binary data, Diagnostics, Convolutional neural networks, X-rays, Databases, Data modeling, Iron, X-ray imaging, Image classification

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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.

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