{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:20:06Z","timestamp":1772166006094,"version":"3.50.1"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T00:00:00Z","timestamp":1599609600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T00:00:00Z","timestamp":1599609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NO.81771904"],"award-info":[{"award-number":["NO.81771904"]}],"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":"crossref","award":["No.61828205"],"award-info":[{"award-number":["No.61828205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Research and Development Project of Xuzhou Science and Technology Bureau","award":["KC19143"],"award-info":[{"award-number":["KC19143"]}]},{"name":"Jiangsu Postdoctoral Science Foundation","award":["1701061B"],"award-info":[{"award-number":["1701061B"]}]},{"name":"Jiangsu Postdoctoral Science Foundation","award":["2017107007"],"award-info":[{"award-number":["2017107007"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurately determining the softness level of pituitary tumors preoperatively by using their image textures can provide a basis for surgical options and prognosis. Existing methods for this problem require manual intervention, which could hinder the efficiency and accuracy considerably.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We present an automatic method for diagnosing the texture of pituitary tumors using unbalanced sequence image data. Firstly, for the small sample problem in our pituitary tumor MRI image dataset where T1 and T2 sequence data are unbalanced (due to data missing) and under-sampled, our method uses a CycleGAN (Cycle-Consistent Adversarial Networks) model for domain conversion to obtain fully sampled MRI spatial sequence. Then, it uses a DenseNet (Densely Connected Convolutional Networks)-ResNet(Deep Residual Networks) based Autoencoder framework to optimize the feature extraction process for pituitary tumor image data. Finally, to take advantage of sequence data, it uses a CRNN (Convolutional Recurrent Neural Network) model to classify pituitary tumors based on their predicted softness levels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experiments show that our method is the best in terms of efficiency and accuracy (91.78%) compared to other methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We propose a semi-supervised method for grading pituitary tumor texture. This method can accurately determine the softness level of pituitary tumors, which provides convenience for surgical selection and prognosis, and improves the diagnostic efficiency of pituitary tumors.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-020-01230-x","type":"journal-article","created":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T08:12:45Z","timestamp":1599725565000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Semi-supervised method for image texture classification of pituitary tumors via CycleGAN and optimized feature extraction"],"prefix":"10.1186","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1676-940X","authenticated-orcid":false,"given":"Hong","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianhao","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihe","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,9]]},"reference":[{"issue":"7","key":"1230_CR1","first-page":"576","volume":"42","author":"YM Mu","year":"2017","unstructured":"Mu YM. Pituitary adenomas: an overview of clinical features, diagnosis and treatment. Med J Chin Peoples Liberation Army. 2017;42(7):576\u201382.","journal-title":"Med J Chin Peoples Liberation Army"},{"issue":"2","key":"1230_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11060-016-2124-y","volume":"130","author":"H Singh","year":"2016","unstructured":"Singh H, et al. Resection of pituitary tumors: endoscopic versus microscopic. J Neuro-Oncol. 2016;130(2):1\u20139.","journal-title":"J Neuro-Oncol"},{"issue":"3","key":"1230_CR3","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/s11102-016-0706-5","volume":"19","author":"JD Hughes","year":"2016","unstructured":"Hughes JD, et al. Magnetic resonance elastography detects tumoral consistency in pituitary macroadenomas. Pituitary. 2016;19(3):286\u201392.","journal-title":"Pituitary"},{"issue":"S1","key":"1230_CR4","doi-asserted-by":"publisher","first-page":"S45","DOI":"10.1017\/cjn.2015.203","volume":"42","author":"RA Won Hyung","year":"2015","unstructured":"Won Hyung RA, et al. Natural history of the anterior visual pathway after surgical decompression in patients with pituitary tumors. Can J Neurol Sci. 2015;42(S1):S45.","journal-title":"Can J Neurol Sci"},{"key":"1230_CR5","doi-asserted-by":"publisher","unstructured":"Luo W, et al. MRI diagnosis and image characteristics of invasive pituitary adenomas. J Mod Oncol. 2014. https:\/\/doi.org\/10.3969\/j.issn.1672-4992.2014.11.64.","DOI":"10.3969\/j.issn.1672-4992.2014.11.64"},{"key":"1230_CR6","first-page":"1","volume":"5","author":"HJWL Aerts","year":"2014","unstructured":"Aerts HJWL, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:1\u20138.","journal-title":"Nat Commun"},{"issue":"15","key":"1230_CR7","doi-asserted-by":"publisher","first-page":"4259","DOI":"10.1158\/1078-0432.CCR-16-2910","volume":"23","author":"B Zhang","year":"2017","unstructured":"Zhang B, et al. Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res. 2017;23(15):4259.","journal-title":"Clin Cancer Res"},{"key":"1230_CR8","doi-asserted-by":"publisher","first-page":"8914","DOI":"10.1109\/ACCESS.2016.2624938","volume":"4","author":"Wang","year":"2016","unstructured":"Wang, Ge. A perspective on deep imaging. IEEE Access. 2016;4:8914\u201324.","journal-title":"IEEE Access"},{"key":"1230_CR9","doi-asserted-by":"crossref","unstructured":"Xu S, Wu H, Bie R. CXNet-m1: anomaly detection on chest X-rays with image-based deep learning. IEEE Access. 2018:1.","DOI":"10.1109\/ACCESS.2018.2885997"},{"issue":"8","key":"1230_CR10","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1109\/LSP.2014.2384196","volume":"22","author":"W Luo","year":"2015","unstructured":"Luo W, et al. Locality-constrained sparse auto-encoder for image classification. IEEE Signal Proc Let. 2015;22(8):1070\u20133.","journal-title":"IEEE Signal Proc Let"},{"issue":"1","key":"1230_CR11","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/TMI.2015.2458702","volume":"35","author":"J Xu","year":"2016","unstructured":"Xu J, et al. Stacked Sparse Autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE T Med Imaging. 2016;35(1):119.","journal-title":"IEEE T Med Imaging"},{"key":"1230_CR12","series-title":"Presented at ICCV","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244","volume-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","author":"JY Zhu","year":"2017","unstructured":"Zhu J. Y, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. Presented at ICCV. 2017. [Online]. Available: https:\/\/arxiv.org\/abs\/1703.10593."},{"key":"1230_CR13","series-title":"Presented at CVPR IEEE Computer Society","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243","volume-title":"Densely connected convolutional networks","author":"G Huang","year":"2017","unstructured":"Huang G, et al. Densely connected convolutional networks. Presented at CVPR IEEE Computer Society. 2017. [Online]. Available: https:\/\/arxiv.org\/abs\/1608.06993."},{"key":"1230_CR14","series-title":"Presented at CVPR","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90","volume-title":"Deep residual learning for image recognition","author":"K He","year":"2016","unstructured":"He K, et al. Deep residual learning for image recognition. Presented at CVPR. 2016. [Online]. Available: https:\/\/arxiv.org\/abs\/1512.03385."},{"issue":"11","key":"1230_CR15","doi-asserted-by":"publisher","first-page":"2298","DOI":"10.1109\/TPAMI.2016.2646371","volume":"39","author":"B Shi","year":"2015","unstructured":"Shi B, Bai X, Yao C. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE T Pattern Anal. 2015;39(11):2298\u2013304.","journal-title":"IEEE T Pattern Anal"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01230-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-020-01230-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01230-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T19:31:27Z","timestamp":1631129487000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-020-01230-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,9]]},"references-count":15,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1230"],"URL":"https:\/\/doi.org\/10.1186\/s12911-020-01230-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.2.18161\/v5","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.2.18161\/v4","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.2.18161\/v3","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.2.18161\/v2","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.2.18161\/v1","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,9]]},"assertion":[{"value":"13 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"We have obtained a permission from the Imaging Department of the Affiliated Hospital of Xuzhou Medical University and the study has been approved by the Ethics Committee of Xuzhou Medical University. We have obtained verbal informed consent from all participants in the study according to the wishes of the participants. For minors, we have obtained the consent of their parents or legal guardians. The Ethics Committee of Xuzhou Medical University has approved this procedure. What we need to declare is that pituitary tumor dataset used in our study has followed all the procedures required by the Chinese government\u2019s law. The data has been strictly reviewed by those in charge of such issues and all sensitive information has been removed. The study is purely for research purpose and does not have any dispute of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"215"}}