{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T21:29:39Z","timestamp":1775078979361,"version":"3.50.1"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319469751","type":"print"},{"value":"9783319469768","type":"electronic"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-46976-8_9","type":"book-chapter","created":{"date-parts":[[2016,9,26]],"date-time":"2016-09-26T03:43:16Z","timestamp":1474861396000},"page":"77-85","source":"Crossref","is-referenced-by-count":147,"title":["Fully Convolutional Network for Liver Segmentation and Lesions Detection"],"prefix":"10.1007","author":[{"given":"Avi","family":"Ben-Cohen","sequence":"first","affiliation":[]},{"given":"Idit","family":"Diamant","sequence":"additional","affiliation":[]},{"given":"Eyal","family":"Klang","sequence":"additional","affiliation":[]},{"given":"Michal","family":"Amitai","sequence":"additional","affiliation":[]},{"given":"Hayit","family":"Greenspan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,9,27]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","unstructured":"Ben-Cohen, A., Klang, E., Amitai, M., Greenspan, H.: Sparsity-based liver metastases detection using learned dictionaries. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1195\u20131198 (2016)","DOI":"10.1109\/ISBI.2016.7493480"},{"key":"9_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-24574-4_1","volume-title":"MICCAI 2015","author":"T Brosch","year":"2015","unstructured":"Brosch, T., Yoo, Y., Tang, L.Y.W., Li, D.K.B., Traboulsee, A., Tam, R.: Deep convolutional encoder networks for multiple sclerosis lesion segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 3\u201311. Springer, Heidelberg (2015)"},{"key":"9_CR3","unstructured":"Deng, X., Du, G.: Editorial: 3D segmentation in the clinic: a grand challenge II-liver tumor segmentation. In: MICCAI Workshop (2008)"},{"issue":"8","key":"9_CR4","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1109\/TMI.2009.2013851","volume":"28","author":"T Heimann","year":"2009","unstructured":"Heimann, T., et al.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. Med. Imaging 28(8), 1251\u20131265 (2009)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR5","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"11","key":"9_CR6","doi-asserted-by":"publisher","first-page":"146","DOI":"10.4236\/jcc.2015.311023","volume":"3","author":"W Li","year":"2015","unstructured":"Li, W., Jia, F., Hu, Q.: Automatic segmentation of liver tumor in CT images with deep convolutional neural networks. J. Comput. Commun. 3(11), 146 (2015)","journal-title":"J. Comput. Commun."},{"key":"9_CR7","doi-asserted-by":"publisher","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Roth, H., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., Summers, R.: Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans. Med. Imaging, (2015, pre-print)","DOI":"10.1109\/TMI.2015.2482920"},{"issue":"4","key":"9_CR9","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/s11548-013-0949-9","volume":"9","author":"L Rusko","year":"2014","unstructured":"Rusko, L., Perenyi, A.: Automated liver lesion detection in CT images based on multi-level geometric features. Int. J. Comput. Assist. Radiol. Surg. 9(4), 577\u2013593 (2014)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Setio, A.A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S., Wille, M.W., Naqibullah, M., Sanchez, C., van Ginneken, B.: Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging, (2016, pre-print)","DOI":"10.1109\/TMI.2016.2536809"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Shimizu, A., et al.: Ensemble segmentation using AdaBoost with application to liver lesion extraction from a CT volume. In: Proceedings of Medical Imaging Computing Computer Assisted Intervention Workshop on 3D Segmentation in the Clinic: A Grand Challenge II, New York (2008)","DOI":"10.54294\/wrtw01"},{"key":"9_CR12","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"9_CR13","doi-asserted-by":"publisher","unstructured":"Vedaldi, A., Lenc, K.: MatConvNet: convolutional neural networks for matlab. In: Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, pp. 689\u2013692 (2015)","DOI":"10.1145\/2733373.2807412"},{"key":"9_CR14","unstructured":"The World Health Report, World Health Organization (2014)"}],"container-title":["Lecture Notes in Computer Science","Deep Learning and Data Labeling for Medical Applications"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-46976-8_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T04:21:38Z","timestamp":1657340498000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-46976-8_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319469751","9783319469768"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-46976-8_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016]]}}}