27 February 2018 Automatic detection of anatomical regions in frontal x-ray images: comparing convolutional neural networks to random forest
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Abstract
Most algorithms to detect and identify anatomical structures in medical images require either to be initialized close to the target structure, or to know that the structure is present in the image, or to be trained on a homogeneous database (e.g. all full body or all lower limbs). Detecting these structures when there is no guarantee that the structure is present in the image, or when the image database is heterogeneous (mixed configurations), is a challenge for automatic algorithms. In this work we compared two state-of-the-art machine learning techniques in order to determine which one is the most appropriate for predicting targets locations based on image patches. By knowing the position of thirteen landmarks points, labelled by an expert in EOS frontal radiography, we learn the displacement between salient points detected in the image and these thirteen landmarks. The learning step is carried out with a machine learning approach by exploring two methods: Convolutional Neural Network (CNN) and Random Forest (RF). The automatic detection of the thirteen landmarks points in a new image is then obtained by averaging the positions of each one of these thirteen landmarks estimated from all the salient points in the new image. We respectively obtain for CNN and RF, an average prediction error (both mean and standard deviation in mm) of 29 ±18 and 30 ± 21 for the thirteen landmarks points, indicating the approximate location of anatomical regions. On the other hand, the learning time is 9 days for CNN versus 80 minutes for RF. We provide a comparison of the results between the two machine learning approaches.
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R. Olory Agomma, C. Vázquez, T. Cresson, J. De Guise, "Automatic detection of anatomical regions in frontal x-ray images: comparing convolutional neural networks to random forest", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753E (27 February 2018); doi: 10.1117/12.2295214; https://doi.org/10.1117/12.2295214
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