Computer-Aided Medical Image Annotation: Preliminary Results With Liver Lesions in CT

The increasing volume of medical image data, as well as the need for multicenter data consolidation for big data analytics, require computer-aided medical image annotation (CMIA). Majority of the methods proposed so far do not exploit interdependencies between annotations explicitly. They further limit their annotations at a higher level than diagnostics and/or do not consider a standardized lexicon. A radiologist-in-the-loop semi-automatic CMIA system is proposed. It is based on a Bayesian tree structured model, linked to RadLex, and present preliminary results with liver lesions in computed tomography images. The proposed system guides the radiologist to input the most critical information in each iteration and uses a network model to update the full annotation online. The effectiveness of the system using this model-based interactive annotation scheme is shown by contrasting the domainblind and domain-aware models. Preliminary results show that on average 7.50 (out of 29) manual annotations are sufficient for 95% accuracy, which is 32.8% less than the required manual effort when there is no guidance. The results also suggest that the domain-aware models perform better than the domain-blind models learned from data. Further analysis with larger datasets and in domains other than the liver lesions is needed.