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The aim of the study was to develop a tool for automatic 3D detection and segmentation of lymph nodes (LNs) in computed tomography (CT) scans of the thorax using a fully convolutional neural network based on 3D foveal patches.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The training dataset was collected from the Computed Tomography Lymph Nodes Collection of the Cancer Imaging Archive, containing 89 contrast-enhanced CT scans of the thorax. A total number of 4275 LNs was segmented semi-automatically by a radiologist, assessing the entire 3D volume of the LNs. Using this data, a fully convolutional neuronal network based on 3D foveal patches was trained with fourfold cross-validation. Testing was performed on an unseen dataset containing 15 contrast-enhanced CT scans of patients who were referred upon suspicion or for staging of bronchial carcinoma.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The algorithm achieved a good overall performance with a total detection rate of 76.9% for enlarged LNs during fourfold cross-validation in the training dataset with 10.3 false-positives per volume and of 69.9% in the unseen testing dataset. In the training dataset a better detection rate was observed for enlarged LNs compared to smaller LNs, the detection rate for LNs with a short-axis diameter (SAD)\u2009\u2265\u200920\u00a0mm and SAD 5\u201310\u00a0mm being 91.6% and 62.2% (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001), respectively. Best detection rates were obtained for LNs located in Level 4R (83.6%) and Level 7 (80.4%).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed 3D deep learning approach achieves an overall good performance in the automatic detection and segmentation of thoracic LNs and shows reasonable generalizability, yielding the potential to facilitate detection during routine clinical work and to enable radiomics research without observer-bias.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12880-021-00599-z","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T13:14:14Z","timestamp":1618319654000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks"],"prefix":"10.1186","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3694-0235","authenticated-orcid":false,"given":"Andra-Iza","family":"Iuga","sequence":"first","affiliation":[]},{"given":"Heike","family":"Carolus","sequence":"additional","affiliation":[]},{"given":"Anna J.","family":"H\u00f6ink","sequence":"additional","affiliation":[]},{"given":"Tom","family":"Brosch","sequence":"additional","affiliation":[]},{"given":"Tobias","family":"Klinder","sequence":"additional","affiliation":[]},{"given":"David","family":"Maintz","sequence":"additional","affiliation":[]},{"given":"Thorsten","family":"Persigehl","sequence":"additional","affiliation":[]},{"given":"Bettina","family":"Bae\u00dfler","sequence":"additional","affiliation":[]},{"given":"Michael","family":"P\u00fcsken","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"key":"599_CR1","doi-asserted-by":"publisher","first-page":"W54","DOI":"10.2214\/AJR.11.7446","volume":"199","author":"CM Walker","year":"2012","unstructured":"Walker CM, Chung JH, Abbott GF, Little BP, El-Sherief AH, Shepard JAO, et al. 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For the validation dataset ethical approval was waived due to the retrospective design of the study based on preexisting images (ethics committee of the Faculty of Medicine, University of Cologne, reference number 19-1390\/ 07.08.2019).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"DM received speaker\u2019s honoraria from Philips Healthcare. A-II received institutional research support from Philips Healthcare for research. HC, TB and TK\u00a0are employees from Philips Research for technical deployment of the AI algorithms. All other authors were independent researchers and guarantee the correctness of the data and results.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"69"}}