CT Diagnostic Mode-Oriented and Cross Difficulty-Aware Network for Pulmonary Embolism Segmentation

Automatic segmentation of pulmonary embolism (PE) in computed tomography pulmonary angiography (CTPA) facilitates the quantitative assessment of PE severity, which is crucial for accurate and comprehensive diagnosis and reducing the high mortality rate of PE. Recent studies have attempted to reduce segmentation errors by integrating vessel segmentation techniques. However, the PE segmentation performance of these methods is largely limited by inter-tissue similarities and the tiny size of PE, along with variability in the shape and position of PE. To address these issues, we propose a CT diagnostic mode-oriented and cross difficulty-aware network (DMCD-Net) for PE segmentation. Specifically, our DMCD-Net imitates the collaborative diagnostic mode of multi-modal CT to learn intensity differences between PE and surrounding tissues, which can effectively reduce false positive segmentation, especially in cases with tiny size and inter-tissue similarities. Moreover, we introduce a cross difficulty-aware scheme with cross-supervision strategies and a difficulty-aware loss function to enhance focus on difficult segmentation regions arising from the irregular shapes and variable locations of PE. Our DMCD-Net is evaluated on two different hospitals and two public datasets. Extensive experiments demonstrate that DMCD-Net outperforms the state-of-the-art methods and shows better generalizability in PE segmentation.