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Motion management is of utmost importance to ensure the effectiveness of radiation treatment for lung cancer patients. The emerging 4D radiotherapy technique aims to actively manage lung tumor motion by providing a time-dependent treatment. This requires the delineation of tumors on all the 3D phase CT images in 4D CT, which is labor intensive and time consuming. In this study, we propose a novel deep learning-based method to automatically localize and segment lung tumor on 4D CT to facilitate the clinical workflow of 4D radiotherapy. The proposed network, named motion Regionbased Convolutional Neural Networks (R-CNN), consists of four stages, i.e., feature extraction, rough tumor location, fine tumor location, and segmentation within tumor region-of-interest (ROI). To aid the network to get rid of unreasonable detected ROIs, different from traditional mask R-CNN, our proposed method first fed 4D CT with consecutive phases into the backbone to extract tumor motion information and then utilized these motion information to estimate global and local deformation vector fields (DVFs), which is useful for measuring the movement of the tumor ROI between each two phases. Our method was tested on 20 patients’ lung 4D CT images in this study. Five metrics were used for quantitative evaluation, i.e., center-of-mass distance (COMD), Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), mean surface distance (MSD), and volume difference (VD) calculated between the manual tumor contour and the contour obtained by our method. Averaged over the 20 cases, our method yielded 1.14±0.95 mm on COMD, 0.76±0.25 on DSC, 2.29±1.31 mm on HD95, 0.75±0.44 mm on MSD, and 0.51±0.51 cc on VD. These results have demonstrated the feasibility and efficacy of our motion R-CNN method for automatic localization and segmentation of lung tumors on 4D CT images. Our method is also expected to be applicable to in-treatment real-time volumetric imaging to provide 3D markerless tumor tracking during treatment delivery.
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