  Fast and accurate anatomical landmark detection can benefit many medical
image analysis methods. Here, we propose a method to automatically detect
anatomical landmarks in medical images. Automatic landmark detection is
performed with a patch-based fully convolutional neural network (FCNN) that
combines regression and classification. For any given image patch, regression
is used to predict the 3D displacement vector from the image patch to the
landmark. Simultaneously, classification is used to identify patches that
contain the landmark. Under the assumption that patches close to a landmark can
determine the landmark location more precisely than patches farther from it,
only those patches that contain the landmark according to classification are
used to determine the landmark location. The landmark location is obtained by
calculating the average landmark location using the computed 3D displacement
vectors. The method is evaluated using detection of six clinically relevant
landmarks in coronary CT angiography (CCTA) scans: the right and left ostium,
the bifurcation of the left main coronary artery (LM) into the left anterior
descending and the left circumflex artery, and the origin of the right,
non-coronary, and left aortic valve commissure. The proposed method achieved an
average Euclidean distance error of 2.19 mm and 2.88 mm for the right and left
ostium respectively, 3.78 mm for the bifurcation of the LM, and 1.82 mm, 2.10
mm and 1.89 mm for the origin of the right, non-coronary, and left aortic valve
commissure respectively, demonstrating accurate performance. The proposed
combination of regression and classification can be used to accurately detect
landmarks in CCTA scans.
