Automatic and Interactive Segmentation of Pulmonary Lobes and Nodules in Chest CT Images

B. Lassen-Schmidt

• Promotor: B. van Ginneken and H. Hahn
• Copromotor: E. van Rikxoort and J. Kuhnigk
• Graduation year: 2015
• Radboud University, Nijmegen

Abstract

This thesis proposes automatic and interactive methods for the segmentation of pulmonary lobes and nodules. A combination of these approaches provides a segmentation workflow starting with automatic methods and continuing with interactive correction if required. Chapter 2 presents a watershed-based automatic segmentation of the lung lobes. It includes information from the pulmonary fissures, bronchi, and vessels. The evaluation was performed on 20 CT scans used in a previous study, allowing a direct comparison. Furthermore, we participated in the lungs and lobe segmentation challenge LOLA11 and applied the method to 55 datasets provided by the organizers of the challenge. Chapter 3 describes a complete segmentation workflow for the pulmonary lobes. The first step is the automatic segmentation presented in Chapter 2. The second step is a 3D geometric method that enables fast and intuitive correction of a given lung lobe segmentation. Also a segmentation from scratch only based on a lung mask is allowed. The boundary between the lobes is represented as a mesh that can be modified by drawing the correct boundary on 2D slices in arbitrary orientation. After each drawing, the mesh is immediately adapted in a 3D region around the user interaction. For evaluation we also participated in the LOLA11 challenge with both the correction and the segmentation from scratch. Two observers applied the approach to correct the automatic segmentation results and one observer did a segmentation from scratch for all of the 55 datasets provided by the challenge. In Chapter 4 an automatic nodule segmentation approach is introduced. A user provides a stroke on the largest diameter of the nodule to initialize the method. Then, a threshold-based region growing is performed based on an intensity analysis around the stroke and surrounding parenchyma. A combination of a connected component analysis and convex hull calculation separates the nodule from the chest wall. Finally, vessels attached to the nodule are removed by morphological operations. The method was evaluated on 59 subsolid publically available nodules provided by LIDC/IDRI. Chapter 5 presents a complete segmentation workflow for pulmonary nodules. As a first step a similar approach to the one introduced in Chapter 4 but with an initial seedpoint instead of a stroke is applied. For the cases with insufficient results an interactive step follows. Here the user can choose between seven precalculated segmentation results. These are also created with the automatic segmentation method but with varying parameters for the threshold-based region growing. This workflow was evaluated on 907 publically available pulmonary nodules provided by LIDC/IDRI. In addition, reliability of volumetric measurement is compared to 2D metrics. Chapters 6 and 7 give a summary of the thesis and a general discussion.