{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:18:41Z","timestamp":1726100321332},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030598600"},{"type":"electronic","value":"9783030598617"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59861-7_11","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T07:07:37Z","timestamp":1601622457000},"page":"101-110","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A 3D+2D CNN Approach Incorporating Boundary Loss for Stroke Lesion Segmentation"],"prefix":"10.1007","author":[{"given":"Yue","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jiong","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Yilong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yifan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Ed X.","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xiaoying","family":"Tang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"issue":"9","key":"11_CR1","doi-asserted-by":"publisher","first-page":"634","DOI":"10.2471\/BLT.16.181636","volume":"94","author":"W Johnson","year":"2016","unstructured":"Johnson, W., Onuma, O., Owolabi, M., et al.: Stroke: a global response is needed. Bull. World Health Organ. 94(9), 634 (2016)","journal-title":"Bull. World Health Organ."},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.3389\/fneur.2018.01060","volume":"9","author":"A Pinto","year":"2018","unstructured":"Pinto, A., Mckinley, R., Alves, V., et al.: Stroke lesion outcome prediction based on MRI imaging combined with clinical information. Front. Neurol. 9, 1060 (2018)","journal-title":"Front. Neurol."},{"issue":"3","key":"11_CR3","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1161\/STROKEAHA.116.015501","volume":"48","author":"SC Cramer","year":"2017","unstructured":"Cramer, S.C., Wolf, S.L., Adams Jr., H.P., et al.: Stroke recovery and rehabilitation research: issues, opportunities, and the National Institutes of Health StrokeNet. Stroke 48(3), 813\u2013819 (2017)","journal-title":"Stroke"},{"issue":"1","key":"11_CR4","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1002\/ana.24309","volume":"77","author":"E Burke Quinlan","year":"2015","unstructured":"Burke Quinlan, E., Dodakian, L., See, J., et al.: Neural function, injury, and stroke subtype predict treatment gains after stroke. Ann. Neuro. 77(1), 132\u2013145 (2015)","journal-title":"Ann. Neuro."},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.neuroimage.2012.07.044","volume":"73","author":"J Crinion","year":"2013","unstructured":"Crinion, J., Holland, A.L., Copland, D.A., Thompson, C.K., Hillis, A.E.: Neuroimaging in aphasia treatment research: quantifying brain lesions after stroke. Neuroimage 73, 208\u2013214 (2013)","journal-title":"Neuroimage"},{"issue":"8","key":"11_CR6","doi-asserted-by":"publisher","first-page":"3077","DOI":"10.1177\/0271678X16683960","volume":"37","author":"SA Tipirneni","year":"2017","unstructured":"Tipirneni, S.A., Christensen, S., Straka, M., et al.: Prediction of final infarct volume on subacute MRI by quantifying cerebral edema in ischemic stroke. J. Cereb. Blood Flow Metab. 37(8), 3077\u20133084 (2017)","journal-title":"J. Cereb. Blood Flow Metab."},{"issue":"16","key":"11_CR7","doi-asserted-by":"publisher","first-page":"4669","DOI":"10.1002\/hbm.24729","volume":"40","author":"KL Ito","year":"2019","unstructured":"Ito, K.L., Kim, H., Liew, S.L.: A comparison of automated lesion segmentation approaches for chronic stroke T1-weighted MRI data. Hum. Brain Mapp. 40(16), 4669\u20134685 (2019)","journal-title":"Hum. Brain Mapp."},{"issue":"4","key":"11_CR8","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1016\/j.neuroimage.2008.03.028","volume":"41","author":"ML Seghier","year":"2008","unstructured":"Seghier, M.L., Ramlackhansingh, A., Crinion, J., Leff, A.P., Price, C.J.: Lesion identification using unified segmentation-normalisation models and fuzzy clustering. NeuroImage 41(4), 1253\u20131266 (2008)","journal-title":"NeuroImage"},{"issue":"4","key":"11_CR9","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.1002\/hbm.23110","volume":"37","author":"D Pustina","year":"2016","unstructured":"Pustina, D., Coslett, H.B., Turkeltaub, P.E., Tustison, N., Schwartz, M.F., Avants, B.: Automated segmentation of chronic stroke lesions using LINDA: lesion identification with neighborhood data analysis. Hum. Brain Mapp. 37(4), 1405\u20131421 (2016)","journal-title":"Hum. Brain Mapp."},{"key":"11_CR10","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.nicl.2015.06.013","volume":"9","author":"B De Haan","year":"2015","unstructured":"De Haan, B., Clas, P., Juenger, H., Wilke, M., Karnath, H.O.: Fast semi-automated lesion demarcation in stroke. NeuroImage Clin. 9, 69\u201374 (2015)","journal-title":"NeuroImage Clin."},{"key":"11_CR11","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.jneumeth.2015.09.019","volume":"257","author":"JC Griffis","year":"2016","unstructured":"Griffis, J.C., Allendorfer, J.B., Szaflarski, J.P.: Voxel-based Gaussian na\u00efve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans. J. Neurosci. Methods 257, 97\u2013108 (2016)","journal-title":"J. Neurosci. Methods"},{"key":"11_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/978-3-030-32248-9_28","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"K Qi","year":"2019","unstructured":"Qi, K., et al.: X-Net: brain stroke lesion segmentation based on depthwise separable convolution and long-range dependencies. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 247\u2013255. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_28"},{"key":"11_CR13","unstructured":"Zhou, Y., Huang, W., Dong, P., et al.: D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation. IEEE\/ACM Trans. Comput. Biol. Bioinform (2019)"},{"key":"11_CR14","doi-asserted-by":"publisher","first-page":"102118","DOI":"10.1016\/j.nicl.2019.102118","volume":"25","author":"Y Xue","year":"2020","unstructured":"Xue, Y., Farhat, F.G., Boukrina, O., et al.: A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images. NeuroImage Clin. 25, 102118 (2020)","journal-title":"NeuroImage Clin."},{"key":"11_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/978-3-030-32248-9_30","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Yang","year":"2019","unstructured":"Yang, H., et al.: CLCI-Net: cross-level fusion and context inference networks for lesion segmentation of chronic stroke. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 266\u2013274. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_30"},{"key":"11_CR16","series-title":"Lecture Notes in Computer Science","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","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.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Wu, J., Zhang, Y., Tang, X.: A multi-atlas guided 3D fully convolutional network for MRI-based subcortical segmentation. In: ISBI, pp. 705\u2013708. IEEE (2019)","DOI":"10.1109\/ISBI.2019.8759286"},{"key":"11_CR18","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.media.2016.07.009","volume":"35","author":"O Maier","year":"2017","unstructured":"Maier, O., Menze, B.H., von der Gablentz, J., H\u00e4ni, L., et al.: ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med. Image Anal. 35, 250\u2013269 (2017)","journal-title":"Med. Image Anal."},{"key":"11_CR19","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.neuroimage.2017.04.041","volume":"170","author":"H Chen","year":"2018","unstructured":"Chen, H., Dou, Q., Yu, L., Qin, J., Heng, P.A.: VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage 170, 446\u2013455 (2018)","journal-title":"NeuroImage"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wu, J., Liu, Y., Chen, Y., Wu, X., Tang, X.: MI-UNet: multi-inputs UNet incorporating brain parcellation for stroke lesion segmentation from T1-weighted magnetic resonance images. IEEE J. Biomed. Health Inform. (2020)","DOI":"10.1109\/JBHI.2020.2996783"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 3DV, pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"11_CR22","unstructured":"Kervadec, H., Bouchtiba, J., Desrosiers, C., et al.: Boundary loss for highly unbalanced segmentation. In: MIDL, pp. 285\u2013296 (2019)"},{"key":"11_CR23","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas, K., Ledig, C., Newcombe, V.F., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61\u201378 (2017)","journal-title":"Med. Image Anal."},{"key":"11_CR24","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.neuroimage.2017.04.039","volume":"170","author":"J Dolz","year":"2018","unstructured":"Dolz, J., Desrosiers, C., Ayed, I.B.: 3D fully convolutional networks for subcortical segmentation in MRI: a large-scale study. NeuroImage 170, 456\u2013470 (2018)","journal-title":"NeuroImage"},{"key":"11_CR25","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.media.2018.02.009","volume":"46","author":"C Lian","year":"2018","unstructured":"Lian, C., Zhang, J., Liu, M., et al.: Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images. Med. Image Anal. 46, 106\u2013117 (2018)","journal-title":"Med. Image Anal."},{"key":"11_CR26","doi-asserted-by":"publisher","first-page":"180011","DOI":"10.1038\/sdata.2018.11","volume":"5","author":"SL Liew","year":"2018","unstructured":"Liew, S.L., Anglin, J.M., Banks, N.W., et al.: A large, open-source dataset of stroke anatomical brain images and manual lesion segmentations. Sci. Data 5, 180011 (2018)","journal-title":"Sci. Data"},{"key":"11_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/978-3-030-32245-8_7","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"C Li","year":"2019","unstructured":"Li, C., Sun, H., Liu, Z., Wang, M., Zheng, H., Wang, S.: Learning cross-modal deep representations for multi-modal MR image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 57\u201365. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_7"},{"key":"11_CR28","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: NIPS, pp. 5998\u20136008 (2017)"},{"key":"11_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/978-3-030-32248-9_34","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Wu","year":"2019","unstructured":"Wu, J., Zhang, Y., Tang, X.: A joint 3D+2D fully convolutional framework for subcortical segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 301\u2013309. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_34"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59861-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T22:05:38Z","timestamp":1639087538000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59861-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030598600","9783030598617"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59861-7_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mlmi2020.web.unc.edu\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"101","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":"68","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":"0","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":"67% - 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":"2.04","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":"3.43","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}