{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:52:29Z","timestamp":1742914349237,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031761591"},{"type":"electronic","value":"9783031761607"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-76160-7_9","type":"book-chapter","created":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T04:53:08Z","timestamp":1735188788000},"page":"97-105","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Automatic Cascaded Model for\u00a0Hemorrhagic Stroke Segmentation and\u00a0Hemorrhagic Volume Estimation"],"prefix":"10.1007","author":[{"given":"Weijin","family":"Xu","sequence":"first","affiliation":[]},{"given":"Zhuang","family":"Sha","sequence":"additional","affiliation":[]},{"given":"Huihua","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Rongcai","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Zhanying","family":"Li","sequence":"additional","affiliation":[]},{"given":"Wentao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Ruisheng","family":"Su","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,27]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Ajoolabady, A., et\u00a0al.: Targeting autophagy in ischemic stroke: from molecular mechanisms to clinical therapeutics. Pharmacol. Therapeutics 107848 (2021)","DOI":"10.1016\/j.pharmthera.2021.107848"},{"key":"9_CR2","unstructured":"Donkor, E.S., et\u00a0al.: Stroke in the century: a snapshot of the burden, epidemiology, and quality of life. Stroke Res. Treat. 2018 (2018)"},{"issue":"10","key":"9_CR3","doi-asserted-by":"publisher","first-page":"1818","DOI":"10.1016\/j.ajem.2018.12.036","volume":"37","author":"LB Dsouza","year":"2019","unstructured":"Dsouza, L.B., et al.: Abc\/2 estimation in intracerebral hemorrhage: a comparison study between emergency radiologists and emergency physicians. Am. J. Emerg. Med. 37(10), 1818\u20131822 (2019)","journal-title":"Am. J. Emerg. Med."},{"key":"9_CR4","doi-asserted-by":"publisher","unstructured":"Feng, R., Badgeley, M., Mocco, J., Oermann, E.K.: Deep learning guided stroke management: a review of clinical applications. J. NeuroInterventional Surgery 10(4), 358\u2013362 (2018). https:\/\/doi.org\/10.1136\/neurintsurg-2017-013355, http:\/\/jnis.bmj.com\/lookup\/doi\/10.1136\/neurintsurg-2017-013355","DOI":"10.1136\/neurintsurg-2017-013355"},{"key":"9_CR5","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Gordon, G.J., Dunson, D.B., Dud\u00edk, M. (eds.) Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, USA, April 11-13, 2011. JMLR Proceedings, vol.\u00a015, pp. 315\u2013323. JMLR.org (2011). http:\/\/proceedings.mlr.press\/v15\/glorot11a\/glorot11a.pdf"},{"key":"9_CR6","doi-asserted-by":"publisher","unstructured":"Grewal, M., Srivastava, M.M., Kumar, P., Varadarajan, S.: Radnet: Radiologist level accuracy using deep learning for hemorrhage detection in ct scans. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 281\u2013284 (2018). https:\/\/doi.org\/10.1109\/ISBI.2018.8363574","DOI":"10.1109\/ISBI.2018.8363574"},{"key":"9_CR7","doi-asserted-by":"publisher","unstructured":"He, X., Chen, K., Hu, K., Chen, Z., Li, X., Gao, X.: Hmoe-net: hybrid multi-scale object equalization network for intracerebral hemorrhage segmentation in CT images. In: Park, T., et al. (eds.) IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Virtual Event, South Korea, 16-19 December 2020, pp. 1006\u20131009. IEEE (2020). https:\/\/doi.org\/10.1109\/BIBM49941.2020.9313439","DOI":"10.1109\/BIBM49941.2020.9313439"},{"key":"9_CR8","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. PMLR (2015)"},{"key":"9_CR9","unstructured":"Lee, C., Xie, S., Gallagher, P.W., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: Lebanon, G., Vishwanathan, S.V.N. (eds.) Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2015, San Diego, California, USA, May 9-12, 2015. JMLR Workshop and Conference Proceedings, vol.\u00a038. JMLR.org (2015). http:\/\/proceedings.mlr.press\/v38\/lee15a.html"},{"issue":"1","key":"9_CR10","doi-asserted-by":"publisher","first-page":"20546","DOI":"10.1038\/s41598-020-77441-z","volume":"10","author":"JY Lee","year":"2020","unstructured":"Lee, J.Y., Kim, J.S., Kim, T.Y., Kim, Y.S.: Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm. Sci. Rep. 10(1), 20546 (2020)","journal-title":"Sci. Rep."},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Lewick, T., Kumar, M., Hong, R., Wu, W.: Intracranial hemorrhage detection in CT scans using deep learning. In: 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService), pp. 169\u2013172. IEEE (2020)","DOI":"10.1109\/BigDataService49289.2020.00033"},{"key":"9_CR12","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings. OpenReview.net (2017). https:\/\/openreview.net\/forum?id=Skq89Scxx"},{"key":"9_CR13","doi-asserted-by":"publisher","first-page":"104320","DOI":"10.1016\/j.bspc.2022.104320","volume":"80","author":"Y Ma","year":"2023","unstructured":"Ma, Y., et al.: Iha-net: an automatic segmentation framework for computer-tomography of tiny intracerebral hemorrhage based on improved attention u-net. Biomed. Signal Process. Control 80, 104320 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"3","key":"9_CR16","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1080\/02841850802647039","volume":"50","author":"CW Wang","year":"2009","unstructured":"Wang, C.W., Juan, C.J., Liu, Y.J., Hsu, H.H., Liu, H.S., Chen, C.Y., Hsueh, C.J., Lo, C.P., Kao, H.W., Huang, G.S.: Volume-dependent overestimation of spontaneous intracerebral hematoma volume by the abc\/2 formula. Acta Radiol. 50(3), 306\u2013311 (2009)","journal-title":"Acta Radiol."},{"issue":"9","key":"9_CR17","doi-asserted-by":"publisher","first-page":"2470","DOI":"10.1161\/STROKEAHA.114.007343","volume":"46","author":"AJ Webb","year":"2015","unstructured":"Webb, A.J., et al.: Accuracy of the ABC\/2 score for intracerebral hemorrhage: systematic review and analysis of MISTIE, CLEAR-IVH, and CLEAR III, and clear iii. Stroke 46(9), 2470\u20132476 (2015)","journal-title":"Stroke"},{"key":"9_CR18","doi-asserted-by":"publisher","unstructured":"Xiao, X., Lian, S., Luo, Z., Li, S.: Weighted res-unet for high-quality retina vessel segmentation. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 327\u2013331 (2018). https:\/\/doi.org\/10.1109\/ITME.2018.00080","DOI":"10.1109\/ITME.2018.00080"},{"issue":"11","key":"9_CR19","doi-asserted-by":"publisher","first-page":"3433","DOI":"10.1161\/STROKEAHA.114.007095","volume":"45","author":"X Xu","year":"2014","unstructured":"Xu, X., et al.: Comparison of the tada formula with software slicer: precise and low-cost method for volume assessment of intracerebral hematoma. Stroke 45(11), 3433\u20133435 (2014)","journal-title":"Stroke"},{"issue":"3","key":"9_CR20","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich, P.A., et al.: User-guided 3d active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116\u20131128 (2006). https:\/\/doi.org\/10.1016\/j.neuroimage.2006.01.015","journal-title":"Neuroimage"}],"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\/978-3-031-76160-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T05:02:43Z","timestamp":1735189363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-76160-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031761591","9783031761607"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-76160-7_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}