Deep learning models for pulmonary embolism segmentation on dual-energy CT: performance analysis and image quality correlation

To evaluate the segmentation performance of dual-energy computed tomography (CT) reconstructed images for pulmonary embolism (PE) and investigate the relationship between segmentation performance and image quality. A retrospective study analyzed dual-energy computed tomography pulmonary angiography (CTPA) data from 57 patients, divided into two datasets (Dataset1 and Dataset2) based on case complexity. Virtual monoenergetic images (VMIs) at 40 keV, 60 keV, 80 keV, and 100 keV, along with iodine map, were reconstructed and manually segmented by radiologists to delineate PE. After augmentation, 18,490 images were split into training and test sets. Five deep learning models were applied to segment different dual-energy reconstructed images, were evaluated using Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Recall, and Precision. Image quality was evaluated, and its correlations with segmentation performance were analyzed using Pearson, Spearman, and Kendall correlation coefficients. In Dataset 1, the 80 keV images achieved the best segmentation performance, with a DSC of 0.565 ± 0.032, recall of 0.596 ± 0.060, IoU of 0.439 ± 0.035, and precision of 0.634 ± 0.059. In Dataset 2, the 60 keV images performed best, with corresponding values of 0.656 ± 0.031, 0.663 ± 0.035, 0.533 ± 0.034, and 0.719 ± 0.034. Among all five segmentation models, Attention UNet consistently achieved the highest performance, with a DSC of 0.545 ± 0.046 on Dataset 1 and 0.649 ± 0.045 on Dataset 2. Segmentation performance was strongly correlated with subjective opinion score, with Pearson, Spearman, and Kendall correlation coefficients of 0.983, 0.800, and 0.667. The 60 keV and 80 keV VMIs consistently demonstrated the most favorable and stable performance trends across datasets of varying complexities, with Attention UNet outperforming the other models. Furthermore, segmentation performance was strongly correlated with subjective opinion score, whereas its correlation with objective image quality metrics such as SNR and CNR was weak and negative.