Supervised recursive segmentation of volumetric CT images for 3D reconstruction of lung and vessel tree

Three dimensional reconstruction of lung and vessel tree has great significance to 3D observation and quantitative analysis for lung diseases. This paper presents non-sheltered 3D models of lung and vessel tree based on a supervised semi-3D lung tissues segmentation method. A recursive strategy based on geometric active contour is proposed instead of the "coarse-to-fine" framework in existing literature to extract lung tissues from the volumetric CT slices. In this model, the segmentation of the current slice is supervised by the result of the previous one slice due to the slight changes between adjacent slice of lung tissues. Through this mechanism, lung tissues in all the slices are segmented fast and accurately. The serious problems of left and right lungs fusion, caused by partial volume effects, and segmentation of pleural nodules can be settled meanwhile during the semi-3D process. The proposed scheme is evaluated by fifteen scans, from eight healthy participants and seven participants suffering from early-stage lung tumors. The results validate the good performance of the proposed method compared with the "coarse-to-fine" framework. The segmented datasets are utilized to reconstruct the non-sheltered 3D models of lung and vessel tree.