Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net.

We propose a novel airway segmentation method in volumetric chest computed tomography (CT) and evaluate its performance on multiple datasets. The segmentation is performed voxel-by-voxel by a 2.5D convolutional neural net (2.5D CNN) trained in a supervised manner. To enhance the accuracy of the segmented airway tree, we simultaneously took three adjacent slices in each of the orthogonal directions including axial, sagittal, and coronal and fine-tuned the parameters that influence the tree length and the number of leakage. The gold standard of airway segmentation was generated by a semi-automated method using AVIEW™. The 2.5D CNN was trained and evaluated on a subset of inspiratory thoracic CT scans taken from the Korean obstructive lung disease study, which includes normal subjects and chronic obstructive pulmonary disease patients. The reliability and further practicality of our proposed method was demonstrated in multiple datasets. In eight test datasets collected by the same imaging protocol, the percentage detected tree length, false positive rate, and Dice similarity coefficient of our method were 92.16%, 7.74%, and 0.8997 ± 0.0892, respectively. In 20 test datasets of the EXACT'09 challenge, the percentage detected tree length was 60.1% and the false positive rate was 4.56%. Our fully automated (end-to-end) segmentation method could be applied in radiologic practice.