Automatic slice identification in 3D medical images with a ConvNet regressor

Abstract

Identification of anatomical regions of interest is a prerequisite in many medical image analysis tasks. We propose a method that automatically identifies a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor. In 150 chest CT scans two reference slices were manually identified: one containing the aortic root and another superior to the aortic arch. In two independent experiments, the ConvNet regressor was trained with 100 CTs to determine the distance between each slice and the SOI in a CT. To identify the SOI, a first order polynomial was fitted through the obtained distances. In 50 test scans, the mean distances between the reference and the automatically identified slices were 5.7mm (4.0 slices) for the aortic root and 5.6mm (3.7 slices) for the aortic arch. The method shows similar results for both tasks and could be used for automatic slice identification.