{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:43:22Z","timestamp":1758588202352,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811674754"},{"type":"electronic","value":"9789811674761"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-7476-1_9","type":"book-chapter","created":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T11:09:50Z","timestamp":1635592190000},"page":"89-99","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bone Marrow Cell Segmentation Based on Improved U-Net"],"prefix":"10.1007","author":[{"given":"Lingmin","family":"Jin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaochai","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoyi","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenghua","family":"Teng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuoyong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,31]]},"reference":[{"issue":"7","key":"9_CR1","first-page":"283","volume":"38","author":"Y Han","year":"2011","unstructured":"Han, Y., Yang, N., Miao, Y., et al.: Bone marrow cell segmentation based on color feature weighted filter. Comput. Sci. 38(7), 283\u2013286 (2011). (in Chinese)","journal-title":"Comput. Sci."},{"issue":"3","key":"9_CR2","doi-asserted-by":"publisher","first-page":"187","DOI":"10.2174\/1574893614666190723115832","volume":"15","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Li, Z., Xie, H., et al.: Leukocyte image segmentation based on adaptive histogram thresholding and contour detection. Curr. Bioinform. 15(3), 187\u2013195 (2020)","journal-title":"Curr. Bioinform."},{"issue":"6","key":"9_CR3","doi-asserted-by":"publisher","first-page":"e0130805","DOI":"10.1371\/journal.pone.0130805","volume":"10","author":"C Reta","year":"2015","unstructured":"Reta, C., Altamirano, L., Gonzalez, J.A., et al.: Segmentation and classification of bone marrow cells images using contextual information for medical diagnosis of acute leukemias. PLoS ONE 10(6), e0130805 (2015)","journal-title":"PLoS ONE"},{"issue":"10","key":"9_CR4","first-page":"1","volume":"133","author":"AH Kandil","year":"2016","unstructured":"Kandil, A.H., Hassan, O.A.: Automatic segmentation of acute leukemia cells. Int. J. Comput. Appl. 133(10), 1\u20138 (2016)","journal-title":"Int. J. Comput. Appl."},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Mohammed, E.A., Far, B.H., Mohamed, M., et al.: Application of support vector machine and k-means clustering algorithms for robust chronic lymphocytic leukemia color cell segmentation. In: Proceedings of the IEEE 15th International Conference on e-Health Networking, Applications and Services, pp. 622\u2013626 (2013)","DOI":"10.1109\/HealthCom.2013.6720751"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Khomairoh, N., Sigit, R., Harsono, T., et al.: Segmentation system of acute myeloid leukemia (AML) subtypes on microscopic blood smear image. In: International Electronics Symposium (IES), pp. 565\u2013570 (2020)","DOI":"10.1109\/IES50839.2020.9231651"},{"key":"9_CR7","unstructured":"Chen, L: Research on bone marrow cell recognition technology based on extreme learning machine. China Jiliang Univ. (2014). (in Chinese)"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Ramoser, H., Laurain, V., Bischof, H., et al.: Leukocyte segmentation and classification in blood-smear images. In: IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 3371\u20133374 (2005)","DOI":"10.1109\/IEMBS.2005.1617200"},{"key":"9_CR9","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III, pp. 234\u2013241. Springer International Publishing, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., et al.: Resunet++: an advanced architecture for medical image segmentation. In: IEEE International Symposium on Multimedia (ISM), pp. 225\u20132255 (2019)","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"9_CR11","unstructured":"Iglovikov, V., Shvets, A.: Ternausnet: U-net with VGG11 encoder pre-trained on imagenet for image segmentation (2018). arXiv preprint arXiv:1801.05746"},{"key":"9_CR12","doi-asserted-by":"publisher","first-page":"107006","DOI":"10.1016\/j.asoc.2020.107006","volume":"101","author":"Y Lu","year":"2021","unstructured":"Lu, Y., Qin, X., Fan, H., et al.: WBC-Net: a white blood cell segmentation network based on UNet++ and ResNet. Appl. Soft Comput. 101, 107006 (2021)","journal-title":"Appl. Soft Comput."},{"issue":"7","key":"9_CR13","doi-asserted-by":"publisher","first-page":"e201800488","DOI":"10.1002\/jbio.201800488","volume":"12","author":"H Fan","year":"2019","unstructured":"Fan, H., Zhang, F., Xi, L., et al.: LeukocyteMask: an automated localization and segmentation method for leukocyte in blood smear images using deep neural networks. J. Biophoton. 12(7), e201800488 (2019)","journal-title":"J. Biophoton."},{"key":"9_CR14","doi-asserted-by":"publisher","first-page":"148779","DOI":"10.1109\/ACCESS.2019.2946681","volume":"7","author":"C Zhou","year":"2019","unstructured":"Zhou, C., Fan, H., Li, Z.: Tonguenet: accurate localization and segmentation for tongue images using deep neural networks. IEEE Access 7, 148779\u2013148789 (2019)","journal-title":"IEEE Access"},{"key":"9_CR15","first-page":"113200B","volume":"11320","author":"L Eekelen","year":"2020","unstructured":"Eekelen, L., Pinckaers, H., Hebeda, K.M., et al.: Multi-class semantic cell segmentation and classification of aplasia in bone marrow histology images. Proc. SPIE Med. Imaging 11320, 113200B (2020)","journal-title":"Proc. SPIE Med. Imaging"},{"issue":"6","key":"9_CR16","first-page":"729","volume":"38","author":"F Wu","year":"2020","unstructured":"Wu, F., Lu, L., Lu, D., et al.: Deep learning model for automatic identification of bone marrow red granulocytes. J. Jilin Univ. (Inf. Sci. Ed.) 38(6), 729\u2013736 (2020). (in Chinese)","journal-title":"J. Jilin Univ. (Inf. Sci. Ed.)"},{"key":"9_CR17","unstructured":"Duta, I.C., Liu, L., Zhu, F., et al.: Pyramidal convolution: rethinking convolutional neural networks for visual recognition (2020). arXiv preprint arXiv:2006.11538"},{"key":"9_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01261-8_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Wu","year":"2018","unstructured":"Wu, Y., He, K.: Group normalization. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_1"},{"key":"9_CR19","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., et al.: Attention U-Net: learning where to look for the pancreas (2018). arXiv preprint arXiv:1804.03999"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Pont-Tuset, J., Marques, F.: Measures and meta-measures for the supervised evaluation of image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2131\u20132138 (2013)","DOI":"10.1109\/CVPR.2013.277"},{"issue":"22","key":"9_CR21","doi-asserted-by":"publisher","first-page":"5844","DOI":"10.1002\/cpe.5844","volume":"32","author":"C Zhou","year":"2020","unstructured":"Zhou, C., Fan, H., Zhao, W., et al.: Reconstruction enhanced probabilistic model for semisupervised tongue image segmentation. Concurr. Comput. Pract. Exp. 32(22), 5844 (2020)","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Fan, H., Zhang, F., Li, Z.: AnomalyDAE: dual autoencoder for anomaly detection on attributed networks. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5685\u20135689 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053387"},{"key":"9_CR23","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1109\/TMM.2020.2999182","volume":"23","author":"C Tian","year":"2021","unstructured":"Tian, C., Yong, X., Zuo, W., et al.: Coarse-to-fine CNN for image super-resolution. IEEE Trans. Multimedia 23, 1489\u20131502 (2021)","journal-title":"IEEE Trans. Multimedia"},{"issue":"8","key":"9_CR24","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861\u2013874 (2006)","journal-title":"Pattern Recogn. Lett."},{"key":"9_CR25","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization (2015). arXiv preprint arXiv:1412.6980"}],"container-title":["Communications in Computer and Information Science","Data Mining and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-7476-1_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T22:04:15Z","timestamp":1758578655000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-7476-1_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811674754","9789811674761"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-7476-1_9","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"31 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DMBD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Mining and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dmbd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/dmbd2021\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"258","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}