{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:50:49Z","timestamp":1780764649126,"version":"3.54.1"},"reference-count":20,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T00:00:00Z","timestamp":1636070400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["N182410001"],"award-info":[{"award-number":["N182410001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["61971118"],"award-info":[{"award-number":["61971118"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["N182410001"],"award-info":[{"award-number":["N182410001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971118"],"award-info":[{"award-number":["61971118"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Healthcare Engineering"],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:p>Segmentation of pulmonary vessels in CT\/CTA images can help physicians better determine the patient\u2019s condition and treatment. However, due to the complexity of CT images, existing methods have limitations in the segmentation of pulmonary vessels. In this paper, a method based on the separation of pulmonary vessels in CT\/CTA images is investigated. The method is divided into two steps: in the first step, the lung parenchyma is extracted using the Unet++ algorithm, which can effectively reduce the oversegmentation rate; in the second step, the pulmonary vessels in the lung parenchyma are extracted using nnUnet. According to the obtained lung parenchyma segmentation results, the \u201cAND\u201d operation is performed on the original image and the lung parenchyma segmentation results, and only the blood vessels within the lung parenchyma are segmented, which reduces the interference of external tissues and improves the segmentation accuracy. The experimental data source used CT\/CTA images acquired from the partner hospital. After the experiments were performed on a total of 67 sets of images, the accuracy of CT and CTA images reached 85.1% and 87.7%, respectively. The comparison of whether to segment the lung parenchyma and with other conventional methods was also performed, and the experimental results showed that the algorithm in this paper has high accuracy.<\/jats:p>","DOI":"10.1155\/2021\/5763177","type":"journal-article","created":{"date-parts":[[2021,11,6]],"date-time":"2021-11-06T04:20:18Z","timestamp":1636172418000},"page":"1-7","source":"Crossref","is-referenced-by-count":6,"title":["A Pulmonary Vascular Extraction Algorithm from Chest CT\/CTA Images"],"prefix":"10.1155","volume":"2021","author":[{"given":"Shihui","family":"Xu","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110189, China"},{"name":"College of Computer Science and Engineering, Northeastern University, Shenyang 110189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8307-2717","authenticated-orcid":true,"given":"Ziming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110189, China"},{"name":"College of Computer Science and Engineering, Northeastern University, Shenyang 110189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghua","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northeastern University, Shenyang 110189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Shao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northeastern University, Shenyang 110189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3840-9528","authenticated-orcid":true,"given":"Wenjun","family":"Tan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110189, China"},{"name":"College of Computer Science and Engineering, Northeastern University, Shenyang 110189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","volume-title":"Pulmonary Vascular Segmentation Method, Involves Determining Artery and Vein Type of Unknown Blood Vessel in Vascular Fusion Map According to Connectivity of Unknown Blood Vessel and Arterial Blood Vessel or Venous Blood Vessel","author":"Y. 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