  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.
