Semi-automated Segmentation of Lungs Using the k-means Method in Cine MRI

Abstract

Dynamic magnetic resonance imaging (MRI) provides essential information on the respiratory kinetics in chronic obstructive pulmonary disease (COPD), such as impaired diaphragm and chest wall motions. The purpose of this study was to develop the semi-automated segmentation program of lungs using cine MRI. We enrolled five control participants and five patients with COPD who underwent cine MRI. The coronal balanced FFE images from each subject were used. The procedures were as follows: First, the maximum inspiratory image was selected from the time-sequential series, and the lung area was manually segmented, which was used for a mask image. Second, both mask image and cine image were accumulated to create a weighted cine image. Lung areas were segmented using the k-means method. Finally, lungs were detected as contiguous image regions with similar signal values using the flood-fill technique. We evaluated the correlation coefficients between the lung area segmented by the semi-automated method and those segmented by a pulmonologist. The correlation coefficients between the semi-automated method and the manual segmentations were excellent (r=0.99, p<0.001). The Dice index was also perfect (0.97). The best number of clusters in the k-means method was 8. These results suggested that the new segmentation method can appropriately extract lungs and help analyze respiratory dynamics in patients with COPD.