Lung Segmentation Using Multi-atlas Registration and Graph Cuts

Imaging of the lungs using computed tomography (CT) are used by clinicians to diagnose and study the progression of emphysema associated with patients suffering from chronic obstructive pulmonary disease (COPD). Risk factors for development of COPD are tobacco smoking and exposure to air pollution for longer durations, which may cause destruction of the alveoli in the lungs, leading to lung function decline by reducing the lungs ability to exchange gases. This is visible in chest CT imaging of the lungs, where density and texture of the lungs are altered by the destruction of lung parenchyma (lung tissue) caused by emphysema. Diagnosis of emphysema is made using pulmonary function tests (PFT), such as spirometry, which may be used to classify emphysema severity and classify emphysema into different stages. Recent studies show that lung function decline and emphysema severity may be predicted and classified using computational analysis of the lungs from chest CT scans. As a prerequisite for automated analysis of chest CT scans, the lungs must be segmented, which necessitates an automatic segmentation approach, as manual delineation can be tedious, time-consuming and subjective. This thesis present an automatic method for the segmentation of the lungs from chest CT scans based on multi-atlas registration and graph cuts. The method combines global anatomical shape information, based on multi-atlas registration from a set of individual atlases, with intensity modeling and local voxel neighborhood modeling into a graph cut segmentation framework. A graph is constructed with nodes for each voxel and edge weights based on the spatial prior, intensity model and neighborhood model, and the optimal segmentation is obtained using graph cut optimization, equivalent to a maximum a posterior probability estimation or an energy minimization. The method has previously been applied in studies for segmentation of the hippocampus from MRI and segmentation of head and neck tissue from CT. The performance of the method will be evaluated on a dataset of 19 chest CT scans with ground truth segmentations serving as individual atlases and reference segmentations in a leave-one-out cross-validation experiment using the Dice similarity coefficient (DSC) as metric of performance and compared to a multi-atlas based segmentation method. The method will also be evaluated on a new dataset of high resolution CT (HRCT) scans of 10 healthy subjects and 10 patients with emphysema. The method will be evaluated by visual inspection and quantitatively, where manual ground truth segmentations are available. The graph cut segmentation method performed significantly better compared to the multi-atlas segmentation method in the leave-one-out cross-validation experiment with a DSC of 0.9877 and 0.9858, respectively. The graph cut segmentation method successfully segmented the lungs on the HRCT dataset of the 10 healthy subjects and 10 patients with emphysema and showed better performance than the multi-atlas segmentation in terms of DSC where manual delineation was available. The graph cut method showed comparable performance to other studies on lung segmentation, however, more data from subjects with emphysema and other lung diseases are necessary for direct comparison and further evaluation of performance. The graph cut segmentation method provides an accurate automatic segmentation of the lungs from chest CT scans, providing an objective segmentation method with no manual intervention needed and an alternative to the time-consuming and subjective manual segmentation. The segmentation may serve as the basis for further computational analysis of the lungs.