Abstract
Pulmonary embolism is a life-threatening condition where early diagnosis and precise localization are crucial for improving patient outcomes. While CT pulmonary angiography (CTPA) is the primary method for detecting pulmonary embolism, existing segmentation algorithms struggle to effectively distinguish thrombi from vascular structures in complex 3D CTPA images, often leading to both false positives and false negatives. To address these challenges, the Threshold Adjustment Segmentation Network (TSNet) is proposed to enhance segmentation performance in 3D CTPA images. TSNet incorporates two core modules: the Threshold Adjustment Module (TAD) and the Geometric-Topological Axial Feature Module (GT-AFM). TAD utilizes logarithmic scaling, adaptive adjustments, and nonlinear transformations to optimize the probability distributions of thrombi and vessels, reducing false positives while improving the sensitivity of thrombus detection. GT-AFM integrates geometric features and topological information to enhance the recognition of complex vascular and thrombotic structures, improving spatial feature processing. Experimental results show that TSNet achieves a sensitivity of 0.761 and a false positives per scan of 1.273 at ε = 0 mm. With an increased tolerance of ε = 5 mm, sensitivity improves to 0.878 and false positives per scan decreases to 0.515, significantly reducing false positives. These results indicate that TSNet demonstrates superior segmentation performance under various tolerance levels, showing robustness and a well-balanced trade-off between sensitivity and false positives, making it highly promising for clinical applications.