  Pulmonary embolism (PE) is a prevalent lung disease that can lead to right
ventricular hypertrophy and failure in severe cases, ranking second in severity
only to myocardial infarction and sudden death. Pulmonary artery CT angiography
(CTPA) is a widely used diagnostic method for PE. However, PE detection
presents challenges in clinical practice due to limitations in imaging
technology. CTPA can produce noises similar to PE, making confirmation of its
presence time-consuming and prone to overdiagnosis. Nevertheless, the
traditional segmentation method of PE can not fully consider the hierarchical
structure of features, local and global spatial features of PE CT images. In
this paper, we propose an automatic PE segmentation method called SCUNet++
(Swin Conv UNet++). This method incorporates multiple fusion dense skip
connections between the encoder and decoder, utilizing the Swin Transformer as
the encoder. And fuses features of different scales in the decoder subnetwork
to compensate for spatial information loss caused by the inevitable
downsampling in Swin-UNet or other state-of-the-art methods, effectively
solving the above problem. We provide a theoretical analysis of this method in
detail and validate it on publicly available PE CT image datasets FUMPE and
CAD-PE. The experimental results indicate that our proposed method achieved a
Dice similarity coefficient (DSC) of 83.47% and a Hausdorff distance 95th
percentile (HD95) of 3.83 on the FUMPE dataset, as well as a DSC of 83.42% and
an HD95 of 5.10 on the CAD-PE dataset. These findings demonstrate that our
method exhibits strong performance in PE segmentation tasks, potentially
enhancing the accuracy of automatic segmentation of PE and providing a powerful
diagnostic tool for clinical physicians. Our source code and new FUMPE dataset
are available at https://github.com/JustlfC03/SCUNet-plusplus.
