Semantics and image content integration for pulmonary nodule interpretation in thoracic computed tomography

Useful diagnosis of lung lesions in computed tomography (CT) depends on many factors including the ability of radiologists to detect and correctly interpret the lesions. Computer-aided Diagnosis (CAD) systems can be used to increase the accuracy of radiologists in this task. CAD systems are, however, trained against ground truth and the mechanisms employed by the CAD algorithms may be distinctly different from the visual perception and analysis tasks of the radiologist. In this paper, we present a framework for finding the mappings between human descriptions and characteristics and computed image features. The data in our study were generated from 29 thoracic CT scans collected by the Lung Image Database Consortium (LIDC). Every case was annotated by up to 4 radiologists by marking the contour of nodules and assigning nine semantic terms to each identified nodule; fifty-nine image features were extracted from each segmented nodule. Correlation analysis and stepwise multiple regression were applied to find correlations among semantic characteristics and image features and to generate prediction models for each characteristic based on image features. From our preliminary experimental results, we found high correlations between different semantic terms (margin, texture), and promising mappings from image features to certain semantic terms (texture, lobulation, spiculation, malignancy). While the framework is presented with respect to the interpretation of pulmonary nodules in CT images, it can be easily extended to find mappings for other modalities in other anatomical structures and for other image features.