Xiaomeng Gu1,2, Jiyong Wang2, Jun Zhao1, Qiang Li2,3. 1. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. 2. Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China. 3. Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
Abstract
PURPOSE: The suppression of pulmonary vessels in chest computed tomography (CT) images can enhance the conspicuity of lung nodules, thereby improving the detection rate of early lung cancer. This study aimed to develop two key techniques in vessel suppression, that is, segmentation and removal of pulmonary vessels while preserving the nodules. METHODS: Pulmonary vessel segmentation and removal methods in CT images were developed. The vessel segmentation method used a framework of two cascaded convolutional neural networks (CNNs). A bi-class segmentation network was utilized in the first step to extract high-intensity structures, including both vessels and nonvascular tissues such as nodules. A tri-class segmentation network was employed in the second step to distinguish the vessels from nonvascular tissues (mainly nodules) and the lung parenchyma. In the vessel removal method, the voxels in the segmented vessels were replaced with randomly selected voxels from the surrounding lung parenchyma. The dataset in this study comprised 50 three-dimensional (3D) low-dose chest CT images. The labels for vessel and nodule segmentation were annotated with a semi automatic approach. The two cascaded networks for pulmonary vessel segmentation were trained with CT images of 40 cases and tested with CT images of ten cases. Pulmonary vessels were removed from the ten testing scans based on the predicted segmentation results. In addition to qualitative evaluation to the effects of segmentation and removal, the segmentation results were quantitatively evaluated using Dice coefficient (DICE), Jaccard index (JAC), and volumetric similarity (VS) and the removal results were evaluated using contrast-to-noise ratio (CNR). RESULTS: In the first step of vessel segmentation, the mean DICE, JAC, and VS for high-intensity tissues, including both vessels and nodules, were 0.943, 0.893, and 0.991, respectively. In the second step, all the nodules were separated from the vessels, and the mean DICE, JAC, and VS for the vessels were 0.941, 0.890, and 0.991, respectively. After vessel removal, the mean CNR for nodules was improved from 4.23 (6.26 dB) to 6.95 (8.42 dB). CONCLUSIONS: Quantitative and qualitative evaluations demonstrated that the proposed method achieved a high accuracy for pulmonary vessel segmentation and a good effect on pulmonary vessel suppression.
PURPOSE: The suppression of pulmonary vessels in chest computed tomography (CT) images can enhance the conspicuity of lung nodules, thereby improving the detection rate of early lung cancer. This study aimed to develop two key techniques in vessel suppression, that is, segmentation and removal of pulmonary vessels while preserving the nodules. METHODS:Pulmonary vessel segmentation and removal methods in CT images were developed. The vessel segmentation method used a framework of two cascaded convolutional neural networks (CNNs). A bi-class segmentation network was utilized in the first step to extract high-intensity structures, including both vessels and nonvascular tissues such as nodules. A tri-class segmentation network was employed in the second step to distinguish the vessels from nonvascular tissues (mainly nodules) and the lung parenchyma. In the vessel removal method, the voxels in the segmented vessels were replaced with randomly selected voxels from the surrounding lung parenchyma. The dataset in this study comprised 50 three-dimensional (3D) low-dose chest CT images. The labels for vessel and nodule segmentation were annotated with a semi automatic approach. The two cascaded networks for pulmonary vessel segmentation were trained with CT images of 40 cases and tested with CT images of ten cases. Pulmonary vessels were removed from the ten testing scans based on the predicted segmentation results. In addition to qualitative evaluation to the effects of segmentation and removal, the segmentation results were quantitatively evaluated using Dice coefficient (DICE), Jaccard index (JAC), and volumetric similarity (VS) and the removal results were evaluated using contrast-to-noise ratio (CNR). RESULTS: In the first step of vessel segmentation, the mean DICE, JAC, and VS for high-intensity tissues, including both vessels and nodules, were 0.943, 0.893, and 0.991, respectively. In the second step, all the nodules were separated from the vessels, and the mean DICE, JAC, and VS for the vessels were 0.941, 0.890, and 0.991, respectively. After vessel removal, the mean CNR for nodules was improved from 4.23 (6.26 dB) to 6.95 (8.42 dB). CONCLUSIONS: Quantitative and qualitative evaluations demonstrated that the proposed method achieved a high accuracy for pulmonary vessel segmentation and a good effect on pulmonary vessel suppression.