Automatic Localization of the Lumbar Vertebral Landmarks in CT Images with Context Features

A recent research direction for the localization of anatomical landmarks with learning-based methods is to explore ways to enrich the trained models with context information. Lately, the addition of context features in regression-based approaches has been tried in the literature. In this work, a method is presented for the addition of context features in a regression setting where the locations of many vertebral landmarks are regressed all at once. As this method relies on the knowledge of the centers of the vertebral bodies (VBs), an automatic, endplate-based approach for the localization of the VB centers is also presented. The proposed methods are evaluated on a dataset of 28 lumbar-focused CT images. The VB localization method detects all of the lumbar VBs of the testing set with a mean localization error of 3.2 mm. The multi-landmark localization method is tested on the task of localizing the tips of all the inferior articular processes of the lumbar vertebrae, in addition to their VB centers. The proposed method detects these landmarks with a mean localization error of 3.0 mm