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• W4313444291 abstract "The automatic segmentation of pulmonary vessels from CT images has important significance. However, accurately annotating pulmonary vessels directly in non-contrast CT (NCCT) images is complex and time-consuming. This study aims to draw annotations with contrast-enhanced CT (CECT) images and train a deep-learning model for segmenting pulmonary vessels from NCCT images. Two datasets with 63 CT scans were collected. Dataset D1 included 17 cases annotated in CECT images, 10 cases annotated in NCCT images, and 12 NCCT scans. Dataset D2 consisted of 12 CECT and 12 NCCT scans with annotations. First, annotations drawn in CECT images (Dataset D1) are transferred to NCCT images via spatial registration. Second, a CE-NC-VesselSegNet is proposed and trained using the transferred annotations to segment pulmonary vessels from NCCT images. Finally, the CE-NC-VesselSegNet is evaluated and compared with its counterparts. After registration, the maximum and root mean square error between CECT and NCCT images decreases, while the structural similarity and peak signal-to-noise ratio increase. CE-NC-VesselSegNet can accurately segment pulmonary vessels from NCCT images with a Dice of 0.856. In the external validation using Dataset D2, the CE-NC-VesselSegNet achieves a Dice of 0.738, which is higher compared with that of NC-VesselSegNet trained by D2. Visual inspections have shown that CE-NC-VesselSegNet enables more accurate and continuous segmentation compared with its counterpart. Annotations of pulmonary vessels drawn in CECT images can be transferred to NCCT images via spatial registration. Using these transferred annotations of high quality, a CE-NC-VesselSegNet can be trained to segment pulmonary vessels from NCCT images." @default.
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• W4313444291 date "2023-04-01" @default.
• W4313444291 modified "2023-10-17" @default.
• W4313444291 title "CE-NC-VesselSegNet: Supervised by contrast-enhanced CT images but utilized to segment pulmonary vessels from non-contrast-enhanced CT images" @default.
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• W4313444291 doi "https://doi.org/10.1016/j.bspc.2022.104565" @default.
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