Interactive classification of lung tissue in CT scans by combining prior and interactively obtained training data: a simulation study

We describe an interactive system for classification of normal and seven types of abnormal lung tissue in CT scans from interstitial lung disease patients, using training data from previously annotated scans and annotations by the observer in the scan under investigation. We compared seven different interactive annotation strategies using different combinations of both types of training data, in order to minimize user effort in the interactive annotation process. The lungs in all scans were divided into roughly spherical volumes of interest (VOIs). An observer labeled all VOIs in 21 thoracic CT scans. Leave-one-scan-out experiments that simulated slice-by-slice interactive annotation sessions were performed. The best results were obtained with a strategy in which the simulated user decides for each slice whether to use a classifier trained on pooled data from prior scans or a classifier trained on data from the current scan. In this approach, the labels of 88% of all VOIs were predicted correctly, meaning that only 12% of all labels needed to be changed by the simulated user.