{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T15:26:46Z","timestamp":1785252406809,"version":"3.55.0"},"reference-count":21,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,10,28]],"date-time":"2020-10-28T00:00:00Z","timestamp":1603843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Lung cancer has one of the highest morbidity and mortality rates in the world. Lung nodules are an early indicator of lung cancer. Therefore, accurate detection and image segmentation of lung nodules is of great significance to the early diagnosis of lung cancer. This paper proposes a CT (Computed Tomography) image lung nodule segmentation method based on 3D-UNet and Res2Net, and establishes a new convolutional neural network called 3D-Res2UNet. 3D-Res2Net has a symmetrical hierarchical connection network with strong multi-scale feature extraction capabilities. It enables the network to express multi-scale features with a finer granularity, while increasing the receptive field of each layer of the network. This structure solves the deep level problem. The network is not prone to gradient disappearance and gradient explosion problems, which improves the accuracy of detection and segmentation. The U-shaped network ensures the size of the feature map while effectively repairing the lost features. The method in this paper was tested on the LUNA16 public dataset, where the dice coefficient index reached 95.30% and the recall rate reached 99.1%, indicating that this method has good performance in lung nodule image segmentation.<\/jats:p>","DOI":"10.3390\/sym12111787","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T23:06:12Z","timestamp":1604012772000},"page":"1787","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":122,"title":["Segmentation of Lung Nodules Using Improved 3D-UNet Neural Network"],"prefix":"10.3390","volume":"12","author":[{"given":"Zhitao","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China"},{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tiangong University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China"},{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tiangong University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Geng","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China"},{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tiangong University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China"},{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tiangong University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanbei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China"},{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tiangong University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21551","article-title":"Cancer statistics, 2019","volume":"69","author":"Siegel","year":"2019","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.artmed.2013.11.002","article-title":"Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index","volume":"60","author":"Carvalho","year":"2014","journal-title":"Artif. Intell. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.media.2010.02.004","article-title":"A new computationally efficient CAD system for pulmonary nodule detection in CT imagery","volume":"14","author":"Messay","year":"2010","journal-title":"Med Image Anal."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ding, J., Li, A., and Hu, Z. (2017, January 10\u201314). Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. 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