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 this https URL

自动肺栓塞（PE）分割方法 SCUNet++ 结合了多个融合的稠密跳跃连接来解决 CTPA 技术的局限性和特征分级层次结构问题，实验证明该方法在 PE 分割任务中表现出色，有潜力提高 PE 的自动分割准确性，为临床医生提供强大的诊断工具。

SCUNet++：利用 Swin-UNet 和 CNN 瓶颈混合架构，结合多融合密集跳跃连接，评估肺栓塞 CT 图像分割

SCUNet++: Assessment of Pulmonary Embolism CT Image Segmentation  Leveraging Swin-UNet and CNN Bottleneck Hybrid Architecture with Multi-Fusion  Dense Skip Connection

This study established a feature-enhanced adversarial semi-supervised
semantic segmentation model to automatically annotate pulmonary embolism lesion
areas in computed tomography pulmonary angiogram (CTPA) images. In current
studies, all of the PE CTPA image segmentation methods are trained by
supervised learning. However, the supervised learning models need to be
retrained and the images need to be relabeled when the CTPA images come from
different hospitals. This study proposed a semi-supervised learning method to
make the model applicable to different datasets by adding a small amount of
unlabeled images. By training the model with both labeled and unlabeled images,
the accuracy of unlabeled images can be improved and the labeling cost can be
reduced. Our semi-supervised segmentation model includes a segmentation network
and a discriminator network. We added feature information generated from the
encoder of segmentation network to the discriminator so that it can learn the
similarity between predicted mask and ground truth mask. This HRNet-based
architecture can maintain a higher resolution for convolutional operations so
the prediction of small PE lesion areas can be improved. We used the labeled
open-source dataset and the unlabeled National Cheng Kung University Hospital
(NCKUH) (IRB number: B-ER-108-380) dataset to train the semi-supervised
learning model, and the resulting mean intersection over union (mIOU), dice
score, and sensitivity achieved 0.3510, 0.4854, and 0.4253, respectively on the
NCKUH dataset. Then, we fine-tuned and tested the model with a small amount of
unlabeled PE CTPA images from China Medical University Hospital (CMUH) (IRB
number: CMUH110-REC3-173) dataset. Comparing the results of our semi-supervised
model with the supervised model, the mIOU, dice score, and sensitivity improved
from 0.2344, 0.3325, and 0.3151 to 0.3721, 0.5113, and 0.4967, respectively.

本研究提出了一种基于 HRNet 的特征增强的敌对半监督语义分割模型，通过添加少量未标记图像实现了对不同数据集的适用，从而自动标注计算机断层扫描肺动脉造影（CTPA）图像中肺栓塞病变区域的位置。与当下的标准模型相比，该模型在 mIOU、Dice 系数和灵敏度方面实现了显著优化。