{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:02:06Z","timestamp":1791914526640,"version":"4.3.4"},"reference-count":32,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T00:00:00Z","timestamp":1655769600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The morbidity of pulmonary embolism (PE) is only lower than that of coronary heart disease and hypertension. Early detection, early diagnosis, and timely treatment are the keys to effectively reduce the risk of death. Nevertheless, PE segmentation is still a challenging task at present. The automatic segmentation of PE is particularly important. On the one hand, manual segmentation of PE from a computed tomography (CT) sequence is very time\u2010consuming and prone to misdiagnose. On the other hand, an accurate contour of the location, volume, and shape of PE can help radiotherapists carry out targeted treatment and thus greatly increase the survival rate of patients. Therefore, developing an automatic and efficient PE segmentation approach is an urgent demand in clinical diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>An accurate segmentation of PE is critical for the diagnosis of PE. However, it remains a difficult and relevant problem in the field of medical image processing due to factors like incongruent sizes and shapes of emboli regions, and low contrast between embolisms and other tissues. To address this conundrum, in this study, a deep neural network (CAM\u2010Wnet) that incorporates coordinate attention (CA) mechanisms and pyramid pooling modules (PPMs) is proposed to end\u2010to\u2010end segment PE from CT image.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>CAM\u2010Wnet architecture is composed of coarse U\u2010Net and subdivision U\u2010Net stacked on top of each other. First, the coarse U\u2010Net uses a pretrained VGG\u201019 as an encoder, which can transfer the features learned from ImageNet to other tasks. At the same time, CA residual blocks (CARBs) are introduced into the decoder of the coarse network to obtain a wider range of semantic information and find out the correlation between channels. Then, the multiplied results of input image and preliminary mask are put into the subdivision U\u2010Net for secondary feature distillation, and the encoder and decoder of the subdivision U\u2010Net are both constructed from CARBs, too. The PPMs are used between the encoder and the decoder of two U\u2010Net architectures to utilize global context information and further enhance the feature extraction effect. Finally, the improved focal loss function is used to train the network to further improve the segmentation effect.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this study, we used the doctors\u2019 manual contours of the China\u2010Japan Friendship Hospital dataset to test the proposed architecture. We calculated the Precision, Recall, IoU, and <jats:italic>F<\/jats:italic>1\u2010score to evaluate the accuracy of the architecture for PE segmentation. The segmentation Precision for PE was found to be 0.9703, Recall was 0.963, IoU was 0.9353, and <jats:italic>F<\/jats:italic>1\u2010score was 0.9665. The experimental results show the effectiveness of the proposed method to automatically and accurately segment embolism in lung CT images. Furthermore, we also test the performance of our method on the liver tumor segmentation public dataset, which demonstrates the effectiveness and generalization ability of our method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>CAM\u2010Wnet obtained more global information and semantic information with the introduction of multiscale pooling and attention mechanisms. Experimental results showed that the proposed method effectively improved the segmentation effect of PE in lung CT images and could be applied to assist doctors in clinical treatment.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15719","type":"journal-article","created":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T19:43:30Z","timestamp":1653421410000},"page":"5294-5303","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["CAM\u2010Wnet: An effective solution for accurate pulmonary embolism segmentation"],"prefix":"10.1002","volume":"49","author":[{"given":"Zhenhong","family":"Liu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence Beijing Normal University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongfang","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Information Science and Technology Beijing University of Chemical Technology  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering University of Chemical Technology  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,6,21]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.thromres.2021.08.015"},{"key":"e_1_2_7_3_1","unstructured":"ElikT ZkaramanlH DemirelH.Fire and smoke detection without sensors: image processing based approach.2007 15th European Signal Processing Conference.IEEE;2007:1794\u20101798."},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1997.10473606"},{"key":"e_1_2_7_5_1","volume-title":"The Application and Comprehensive Evaluation of Random\u2010Fuzzy Probability Statistics Method in Slope of Rumei Hydropower Station","author":"Luo B"},{"key":"e_1_2_7_6_1","doi-asserted-by":"crossref","unstructured":"LongJ ShelhamerE DarrellT.Fully convolutional networks for semantic segmentation.Conference on Computer Vision and Pattern Recognition(CVPR);2015.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_2_7_7_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010Net: convolutional networks for biomedical image segmentation.International Conference on Medical Image Computing and Computer\u2010Assisted Intervention.Springer;2015.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_7_8_1","doi-asserted-by":"crossref","unstructured":"XuW LiuH WangX QianY.Liver segmentation in CT based on ResUNet with 3D probabilistic and geometric post process.IEEE International Conference on Signal and Image Processing.IEEE;2019.","DOI":"10.1109\/SIPROCESS.2019.8868690"},{"key":"e_1_2_7_9_1","doi-asserted-by":"crossref","unstructured":"SunH LiC LiuB LiuZ WangM ZhengH FengDD.AUNet: Attention\u2010guided dense\u2010upsampling networks for breast mass segmentation in whole mammograms. 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