{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T16:29:47Z","timestamp":1745252987298,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030117221"},{"type":"electronic","value":"9783030117238"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-11723-8_34","type":"book-chapter","created":{"date-parts":[[2019,1,25]],"date-time":"2019-01-25T13:47:41Z","timestamp":1548424061000},"page":"335-342","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Combining Good Old Random Forest and DeepLabv3+ for ISLES 2018 CT-Based Stroke Segmentation"],"prefix":"10.1007","author":[{"given":"Lasse","family":"B\u00f6hme","sequence":"first","affiliation":[]},{"given":"Frederic","family":"Madesta","sequence":"additional","affiliation":[]},{"given":"Thilo","family":"Sentker","sequence":"additional","affiliation":[]},{"given":"Ren\u00e9","family":"Werner","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,1,26]]},"reference":[{"key":"34_CR1","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., Hani, L., Heinrich, M.P., 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."},{"issue":"121","key":"34_CR2","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1177\/1747493016676285","volume":"12","author":"AG Thrift","year":"2017","unstructured":"Thrift, A.G., Thayabaranathan, T., Howard, G., Howard, V.J., Rothwell, P.M., et al.: Global stroke statistics. Int. J. Stroke 12(121), 13\u201332 (2017)","journal-title":"Int. J. Stroke"},{"key":"34_CR3","unstructured":"ISLES challenge(s). http:\/\/www.isles-challenge.org"},{"key":"34_CR4","doi-asserted-by":"publisher","first-page":"679","DOI":"10.3389\/fneur.2018.00679","volume":"9","author":"S Winzeck","year":"2018","unstructured":"Winzeck, S., Hakim, A., McKinley, R., Pinto, J., Alves, V., et al.: ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Front. Neurol. 9, 679 (2018)","journal-title":"Front. Neurol."},{"key":"34_CR5","doi-asserted-by":"publisher","first-page":"2144","DOI":"10.1161\/01.STR.0000026862.42440.AA","volume":"33","author":"T Tatlisumak","year":"2002","unstructured":"Tatlisumak, T.: Is CT or MRI the method of choice for imaging patients with acute stroke? Why should men divide if fate has united? Stroke 33, 2144\u20132145 (2002)","journal-title":"Stroke"},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Wang, C.-W., Lee, J.-H.: Stroke lesion segmentation of 3D brain MRI using multiple random forests and 3D registration. In: Proceedings of MICCAI-ISLES 2015, pp. 35\u201338 (2015)","DOI":"10.1007\/978-3-319-30858-6_19"},{"key":"34_CR7","unstructured":"Chen, L., Bentley, P., Rueckert, D.: A novel framework for sub-acute stroke lesion segmentation based on random forest. In: Proceedings of MICCAI-ISLES 2015, pp. 9\u201312 (2015)"},{"key":"34_CR8","unstructured":"Maier, O., Wilms, M., Handels, H.: Random forests with selected features for stroke lesion segmentation. In: Proceedings of MICCAI-ISLES 2015, pp. 17\u201322 (2015)"},{"key":"34_CR9","unstructured":"Reza, S.M.S., Pei, L., Iftekharuddin, K.M.: Ischemic stroke lesion segmentation using local gradient and texture features. In: Proceedings of MICCAI-ISLES 2015, pp. 23\u201326 (2015)"},{"key":"34_CR10","unstructured":"Robben, D., Christiaens, D., Rangarajan, J.R., Gelderblom, J., Joris, P., et al.: ISLES challenge 2015: a voxel-wise, cascaded classification approach to stroke lesion segmentation. In: Proceedings of MICCAI-ISLES 2015, pp. 27\u201330 (2015)"},{"key":"34_CR11","doi-asserted-by":"crossref","unstructured":"Halme, H.-L., Korvenoja, A., Salli, E.: ISLES (SISS) challenge 2015: segmentation of stroke lesions using spatial normalization, random forest classification and contextual clustering. In: Proceedings of MICCAI-ISLES 2015, pp. 31\u201334 (2015)","DOI":"10.1007\/978-3-319-30858-6_18"},{"key":"34_CR12","unstructured":"Goetz, M., Weber, C., Maier-Hein, K.: Input data adaptive learning (IDAL) for sub-acute ischemic stroke lesion segmentation. In: Proceedings of MICCAI-ISLES 2015, pp. 39\u201342 (2015)"},{"key":"34_CR13","unstructured":"Mahmood, Q., Basit, A.: Automatic ischemic stroke lesion segmentation in multi-spectral MRI images using random forests classifier. In: Proceedings of MICCAI-ISLES 2015, pp. 43\u201346 (2015)"},{"key":"34_CR14","unstructured":"Jesson, A., Arbel, T.: Hierarchical segmentation of normal and lesional structures combining an ensemble of probabilistic local classifiers and regional random forest classification. In: Proceedings of MICCAI-ISLES 2015, pp. 57\u201362 (2015)"},{"key":"34_CR15","unstructured":"Muschelli, J.: Prediction of ischemic lesions using local image properties and random forests. In: Proceedings of MICCAI-ISLES 2015, pp. 63\u201365 (2015)"},{"key":"34_CR16","unstructured":"Kamnitsas, K., Chen, L., Ledig, C., Rueckert, D., Glocker, B.: Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI. In: Proceedings of MICCAI-ISLES 2015, pp. 13\u201316 (2015)"},{"key":"34_CR17","unstructured":"Dutil, F., Havaei, M., Pal, C., Larochelle, H., Jodoin, P.-M.: A convolutional neural network approach to brain lesion segmentation. In: Proceedings of MICCAI-ISLES 2015, pp. 51\u201356 (2015)"},{"key":"34_CR18","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 \u2014 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":"34_CR19","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"34_CR20","unstructured":"Maier, O.: MedPy \u2013 medical image processing in Python. https:\/\/pypi.org\/project\/MedPy"},{"key":"34_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"34_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Lecture Notes in Computer Science","Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-11723-8_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T15:59:33Z","timestamp":1710345573000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-11723-8_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030117221","9783030117238"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-11723-8_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"26 January 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BrainLes","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International MICCAI Brainlesion Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Granada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.brainlesion-workshop.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"95","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":"92","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":"97% - 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":"3","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","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}