Contextual Deep Regression Network for Volume Estimation in Orbital CT

Abstract

Diseases of the optic nerve cause structural changes observable through clinical computed tomography (CT) imaging. Previous work has shown that multi-atlas methods can be used to segment and extract volumetric measurements from the optic nerve, which are associated with visual disability and disease. In this work, we trained a weakly supervised convolutional neural network to learn optic nerve volumes directly, without segmentation. Furthermore, we explored the role of contextual electronic medical record (EMR) information, specifically ICD-9 codes, to improve optic nerve volume estimation. We constructed a merged network to combine data from imaging as well as EMR and demonstrated that context improved volume prediction, with a 15% increase in explained-variance $\left( {R^{2} } \right)$. Finally, we compared disease prediction models using volumes learned from multi-atlas, CNN, and contextual-CNN. We observed that the predicted optic nerve volume from merge-CNN had an AUC of 0.74 for classification of disease, as compared to an AUC of 0.54 using the multi-atlas metric. This is the first work to show that a contextually derived volume biomarker is more accurate than volume estimations through multi-atlas or weakly supervised image CNN. These results highlight the potential for image processing improvements by incorporating non-imaging data.