A computer-based method of segmenting ground glass nodules in pulmonary CT images: comparison to expert radiologists' interpretations

Abstract

Ground glass nodules (GGNs) have proved especially problematic in lung cancer diagnosis, as despite frequently being malignant they have extremely slow growth rates. In this work, the GGN segmentation results of a computer-based method were compared with manual segmentation performed by two dedicated chest radiologists. CT volumes of 8 patients were acquired by multi-slice CT. 21 pure or mixed GGNs were identified and independently segmented by the computer-based method and by two readers. The computer-based method is initialized by a click point, and uses a Markov random field (MRF) model for segmentation. While the intensity distribution varies for different GGNs, the intensity model used in MRF is adapted for each nodule based on initial estimates. This method was run three times for each nodule using different click points to evaluate consistency. In this work, consistency was defined by the overlap ratio (overlap volume/mean volume). The consistency of the computer-based method with different initial points, with a mean overlap ratio of 0.96±0.02 (95% confidence interval on mean), was significantly higher than the <i>inter</i>-observer consistency between the two radiologists, indicated by a mean overlap ratio of 0.73±0.04. The computer consistency was also significantly higher than the <i>intra</i>-observer consistency of two measurements from the same radiologist, indicated by an overlap ratio of 0.69±0.05 (<i>p-value</i> < 1E-05). The concordance of the computer with the expert interpretation demonstrated a mean overlap ratio of 0.69±0.05. As shown by our data, the consistency provided by the computer-based method is significantly higher than between observers, and the accuracy of the method is no worse than that of one physician’s accuracy with respect to another, allowing more reproducible assessment of nodule growth.