Deep Convolutional Neural Network model for sub-Anatomy specific Landmark detection on Brain MRI

We present a Deep Convolutional neural network (D-CNN) model for brain landmark detection, an important step in planning MRI scans for two anatomies - Corpus callosum and Cerebellum on 3D T1w images. Unlike traditional approaches like segmentation followed by image processing to identify the brain landmark points (AL-Net), we use a D-CNN to directly predict the landmarks. For all the landmarks, we demonstrate that D-CNN can achieve landmark detection with Average Root mean square error(RMSE) <= 6mm (N=100) for all landmarks and is comparable or better than AL-NET (RMSE <=8mm). Results indicate excellent feasibility of the method for clinical usage.