Segmentation of Femurs in X-ray Image with Generative Adversarial Networks

LIANG-HUI FAN, JUN-GANG HAN, YANG JIA, CHEN ZHAO, BIN YANG

Abstract


Segmentation of Femur bone from X-ray images is an indispensable step in computer aided analysis of medical images and orthopaedic examinations. It is more complex than segmentation from CT and MR images, because some of the less dense related tissues are difficult to distinguish from the femur in X-ray images. We present a method based on Generative Adversarial Networks (GAN) to automatically extract the femurs from hip X-ray images. Specifically, we propose a Generative Adversarial Networks for Femur Segmentation to map hip image to femur image with nonlinear relationship and produce accurate segmentation results. In Generative Adversarial Networks for Femur Segmentation, we use the adaptive contours estimation algorithm and absolute deviations loss function to optimize neural networks. Experimental results show that our method is accurate, robust, and achieves an average dice similarity coefficient of 0.995.

Keywords


Femur segmentation, X-ray image segmentation, Generative Adversarial NetworksText


DOI
10.12783/dtetr/ecae2018/27745

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