Detection And Identification Of Lower-Limb Bones In Biplanar X-RAY Images With Arbitrary Field Of View And Various Patient Orientations.

Correctly detecting and identifying bones in radiographic images are the first stages of every orthopaedic procedure. This apparently simple task is mainly performed by human operators before performing more complex operations. Automatic detection and identification of bones in radiographic images, especially if the field of view and the position of the patient are not known a priori, remains a challenging task. In this paper, lower-limb bones are automatically detected and identified on biplanar X-ray images with varying fields of view and two main orientations of the patient with respect to the imaging device. The proposed method uses data augmentation to improve the training of a deep learning method to identify the lower-limb bones. We used 30 biplanar radiographs with varying fields of view to validate the proposed method. We obtained a global accuracy (mean $\pm {std})$ of $96.75 \pm 0.01$% and a Dice coefficient of $93.85\pm 0.02$%, proving the usefulness of the proposed method.