Fig. 3: Segmentation comparison against CTPA, and clinical evaluation for HiPaS. | Nature Communications

Fig. 3: Segmentation comparison against CTPA, and clinical evaluation for HiPaS.

From: Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences

Fig. 3

a Comparison of artery-vein segmentation on paired non-contrast CT and CTPA achieved from DSCTPA. HiPaS can identify arteries and veins directly from non-contrast CT, whose performance is non-inferior to the segmentation on CTPA. b Quantitative comparison of segmentation results between non-contrast CT and paired CTPA from the same patient. Dice similarity coefficient (DSC) is calculated in three scenarios: (1) between segmentation on non-contrast CT and the corresponding annotations; (2) between segmentation on CTPA and the corresponding annotations; and (3) between segmentation from non-contrast CT and CTPA. c Clinical evaluation of HiPaS. Three radiologists from distinct hospitals independently assessed the segmentation results derived from the three methods, nnUNet, semi-automatic segmentation, and HiPaS. The specific method corresponding to the segmentation results remained undisclosed to the radiologists, ensuring unbiased evaluations. The assessment encompassed three key indicators: segmentation accuracy and robustness, vessel branch abundances, and diagnostic assistance (n = 50). Error bars show the standard error of mean (SEM) and the center for the error bars indicates average values. One-side Mann-Whitney U tests were done between each method. P-values are specified as ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001, NS, not significant. Source data are provided as a Source Data file.

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