Background: Volumetric lung tumor segmentation is difficult due to the diversity of the sizes, locations and shapes of lung tumors, as well as the similarity in the intensity with surrounding tissue structures.
Objective: We propose a dual-coupling net for accurate lung tumor segmentation in chest CT images regardless of sizes, locations and shapes of lung tumors.METHODSTo extract shape information from lung tumors and use it as shape prior, three-planar images including axial, coronal, and sagittal planes are trained on 2D-Nets. Two types of window images, lung and mediastinal window images, are trained on 2D-Nets to distinguish lung tumors from the thoracic region and to better separate the boundaries of lung tumors from adjacent tissue structures. To prevent false-positive outliers to adjacent structures and to consider the spatial information of lung tumors, pairs of tumor volume-of-interest (VOI) and tumor shape prior are trained on 3D-Net.RESULTSIn the first experiment, the dual-coupling net had the highest Dice Similarity Coefficient (DSC) of 75.7%, considering the shape prior as well as mediastinal window images to prevent the leakage of adjacent structures while maintaining the shape of the lung tumor, with 18.23% p, 3.7% p, 1.1% p, and 1.77% p higher DSCs than in the 2D-Net, 2.5D-Net, 3D-Net, and single-coupling net results, respectively. In the second experiment with annotations for two clinicians, the dual-coupling net showed outcomes of 67.73% and 65.07% regarding the DSC for each annotation. In the third experiment, the dual-coupling net showed 70.97% for the DSC.CONCLUSIONSThe dual-coupling net enables accurate segmentation by distinguishing lung tumors from surrounding tissue structures and thus yields the highest DSC value.
Keywords: Chest computed tomography (CT); deep learning; lung tumor segmentation; mediastinal window image; shape prior.