Automatic segmentation and identification of solitary pulmonary nodules on follow-up CT scans based on local intensity structure analysis and non-rigid image registration

This paper presents a novel method that can automatically segment solitary pulmonary nodule (SPN) and match such segmented SPNs on follow-up thoracic CT scans. Due to the clinical importance, a physician needs to find SPNs on chest CT and observe its progress over time in order to diagnose whether it is benign or malignant, or to observe the effect of chemotherapy for malignant ones using follow-up data. However, the enormous amount of CT images makes large burden tasks to a physician. In order to lighten this burden, we developed a method for automatic segmentation and assisting observation of SPNs in follow-up CT scans. The SPNs on input 3D thoracic CT scan are segmented based on local intensity structure analysis and the information of pulmonary blood vessels. To compensate lung deformation, we co-register follow-up CT scans based on an affine and a non-rigid registration. Finally, the matches of detected nodules are found from registered CT scans based on a similarity measurement calculation. We applied these methods to three patients including 14 thoracic CT scans. Our segmentation method detected 96.7% of SPNs from the whole images, and the nodule matching method found 83.3% correspondences from segmented SPNs. The results also show our matching method is robust to the growth of SPN, including integration/separation and appearance/disappearance. These confirmed our method is feasible for segmenting and identifying SPNs on follow-up CT scans.