Deep learning-based Automated Localisation of Anterior Commissure and Posterior Commissure Landmarks in 3D space from three-plane 2D MRI localiser slices of the brain

Abstract

The operator first performs the three-plane MRI localiser slices acquisition protocol during brain MRI scan acquisition. Based on various anatomical landmarks such as anterior commissure AC), posterior commissure (PC), and mid-sagittal plane (MSP), the operator plans MRI position and orientation before a full brain MRI scan which is essential for good quality MRI. Automatic localisation of these landmarks is vital to automatise the process and minimise operator error. Prior approaches focused on automated AC and PC detection on 2D mid-sagittal MRI slices. However, improper head positioning leads to improper 2D mid-sagittal MRI slice; therefore, it may impact the localisation error, and the localisation is not in 3D space with respect to brain volume. In the present work, the AC and PC landmarks' locations were predicted in 3D space from three-plane 2D MRI localiser slices using a convolutional neural network-based approach. Six publically available brain MRI datasets were used. The mean AC and PC localisation error obtained was less than 2mm in a within-dataset evaluation and less than 3mm in a cross-dataset evaluation.