Characterizing pulmonary nodule shape using a boundary-region approach

Using computer-calculated features to characterize the shape of suspicious lesions aims to assist the diagnosis of pulmonary nodules; moreover, these computerized features have to be in agreement with radiologists' ratings measuring their human perception of the nodules' shape. In the Lung Image Database Consortium (LIDC), there exists strong disagreement among the radiologists on the ratings of the shape diagnostic characteristics as well as on their drawn outlines of the extent of the nodules. Since shape is often considered a property of the object boundary and the manual boundaries are not consistent among radiologists, new methods are necessary to, first, define regionbased boundaries that use radiologists' outlines as guides and, second, adapt computer-based shape measurements to use regions rather than the traditional nodule segmentation outlines. This paper introduces a method for defining a boundary region of interest by combining radiologist-drawn outlines (the pixel-set difference between the union and intersection of all radiologist-drawn outlines for a specific nodule), then adapts a radial gradient indexing method for use within image regions, and lastly predicts several composite ratings of sets of radiologists for shape-based characteristics: spiculation, lobulation, and sphericity. The prediction of the majority (mode) rating significantly outperforms earlier work on predicting the ratings of individual radiologists. The prediction of spiculation improves to 53% from 41%, lobulation increases to 44% from 38%, and sphericity improves to 58% from 43%. A binary version of the rating has high accuracy but poor Kappa agreement for all three shape characteristics.