Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT

Background: Accurate segmentation of pulmonary nodules on computed tomography (CT) scans plays a crucial role in the evaluation and management of patients with suspicion of lung cancer (LC). When performed manually, not only the process requires highly skilled operators, but is also tiresome and time-consuming. To assist the physician in this task several automated and semi-automated methods have been proposed in the literature. In recent years, in particular, the appearance of deep learning has brought about major advances in the field. Methods: Twenty-four (12 conventional and 12 based on deep learning) semi-automated-'one-click'- methods for segmenting pulmonary nodules on CT were evaluated in this study. The experiments were carried out on two datasets: A proprietary one (383 images from a cohort of 111 patients) and a public one (259 images from a cohort of 100). All the patients had a positive transcript for suspect pulmonary nodules. Results: The methods based on deep learning clearly outperformed the conventional ones. The best performance [Sørensen-Dice coefficient (DSC)] in the two datasets was, respectively, 0.853 and 0.763 for the deep learning methods, and 0.761 and 0.704 for the traditional ones. Conclusions: Deep learning is a viable approach for semi-automated segmentation of pulmonary nodules on CT scans.