Automated anatomical landmark detection ondistal femur surface using convolutional neural network

Accurate localization of the anatomical landmarks on distal femur bone in the 3D medical images is very important for knee surgery planning and biomechanics analysis. However, the landmark identification process is often conducted manually or by using the inserted auxiliaries, which is time-consuming and lacks of accuracy. In this paper, an automatic localization method is proposed to determine positions of initial geometric landmarks on femur surface in the 3D MR images. Based on the results from the convolutional neural network (CNN) classifiers and shape statistics, we use the narrow-band graph cut optimization to achieve the 3D segmentation of femur surface. Finally, the anatomical landmarks are located on the femur according to the geometric cues of surface mesh. Experiments demonstrate that the proposed method is effective, efficient, and reliable to segment femur and locate the anatomical landmarks.