{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T17:35:42Z","timestamp":1790616942561,"version":"4.1.0"},"reference-count":53,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2020,12,30]],"date-time":"2020-12-30T00:00:00Z","timestamp":1609286400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Early detection of lung cancer is of importance since it can increase patients\u2019 chances of survival. To detect nodules accurately during screening, radiologists would commonly take the axial, coronal, and sagittal planes into account, rather than solely the axial plane in clinical evaluation. Inspired by clinical work, the paper aims to develop an accurate deep learning framework for nodule detection by a combination of multiple planes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The nodule detection system is designed in two stages, multiplanar nodule candidate detection, multiscale false positive (FP) reduction. At the first stage, a deeply supervised encoder\u2013decoder network is trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are merged. To further refine results, a three\u2010dimensional multiscale dense convolutional neural network that extracts multiscale contextual information is applied to remove non\u2010nodules. In the public LIDC\u2010IDRI dataset, 888 computed tomography scans with 1186 nodules accepted by at least three of four radiologists are selected to train and evaluate our proposed system via a tenfold cross\u2010validation scheme. The free\u2010response receiver operating characteristic curve is used for performance assessment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed system achieves a sensitivity of 94.2% with 1.0 FP\/scan and a sensitivity of 96.0% with 2.0 FPs\/scan. Although it is difficult to detect small nodules (i.e., &lt;6\u00a0mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall FP rate of 1.0 (2.0) FPs\/scan. At the nodule candidate detection stage, results show that the system with a multiplanar method is capable to detect more nodules compared to using a single plane.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our approach achieves good performance not only for small nodules but also for large lesions on this dataset. This demonstrates the effectiveness of our developed CAD system for lung nodule detection.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.14648","type":"journal-article","created":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T22:39:10Z","timestamp":1607639950000},"page":"733-744","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification"],"prefix":"10.1002","volume":"48","author":[{"given":"Sunyi","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology University Medical Center Groningen University of Groningen 9713 AV Groningen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ludo J.","family":"Cornelissen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University Medical Center Groningen University of Groningen 9713 AV Groningen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaonan","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Radiology Tianjin Medical University Cancer Institute and Hospital National Clinical Research Centre of Cancer 300060 Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueping","family":"Jing","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University Medical Center Groningen University of Groningen 9713 AV Groningen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raymond N. 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