Interactive Pulmonary Lobe Segmentation in CT Images Based on Oriented Derivative of Stick Filter and Surface Fitting Model

Automated approaches for pulmonary lobe segmentation frequently encounter difficulties when applied to clinically significant cases, primarily stemming from factors such as incomplete and blurred pulmonary fissures, unpredictable pathological deformation, indistinguishable pulmonary arteries and veins, and severe damage to the lung trachea. To address these challenges, an interactive and intuitive approach utilizing an oriented derivative of stick (ODoS) filter and a surface fitting model is proposed to effectively extract and repair incomplete pulmonary fissures for accurate lung lobe segmentation in computed tomography (CT) images. First, an ODoS filter was employed in a two-dimensional (2D) space to enhance the visibility of pulmonary fissures using a triple-stick template to match the curvilinear structures across various orientations. Second, a three-dimensional (3D) post-processing pipeline based on a direction partition and integration approach was implemented for the initial detection of pulmonary fissures. Third, a coarse-to-fine segmentation strategy is utilized to eliminate extraneous clutter and rectify missed pulmonary fissures, thereby generating accurate pulmonary fissure segmentation. Finally, considering that pulmonary fissures serve as physical boundaries of the lung lobes, a multi-projection technique and surface fitting model were combined to generate a comprehensive fissure surface for pulmonary lobe segmentation. To assess the effectiveness of our approach, we actively participated in an internationally recognized lung lobe segmentation challenge known as LObe and Lung Analysis 2011 (LOLA11), which encompasses 55 CT scans. The validity of the proposed methodology was confirmed by its successful application to a publicly accessible challenge dataset. Overall, our method achieved an average intersection over union (IoU) of 0.913 for lung lobe segmentation, ranking seventh among all participants so far. Furthermore, experimental outcomes demonstrated excellent performance compared with other methods, as evidenced by both visual examination and quantitative evaluation.