{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:41:36Z","timestamp":1777941696425,"version":"3.51.4"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031716256","type":"print"},{"value":"9783031716263","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T00:00:00Z","timestamp":1729728000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T00:00:00Z","timestamp":1729728000000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-71626-3_1","type":"book-chapter","created":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T16:02:50Z","timestamp":1729699370000},"page":"3-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Fusion of\u00a0Deep and\u00a0Local Features Using Random Forests for\u00a0Neonatal HIE Segmentation"],"prefix":"10.1007","author":[{"given":"Imad","family":"Eddine Toubal","sequence":"first","affiliation":[]},{"given":"Elham","family":"Soltani Kazemi","sequence":"additional","affiliation":[]},{"given":"Gani","family":"Rahmon","sequence":"additional","affiliation":[]},{"given":"Taci","family":"Kucukpinar","sequence":"additional","affiliation":[]},{"given":"Mohamed","family":"Almansour","sequence":"additional","affiliation":[]},{"given":"Mai-Lan","family":"Ho","sequence":"additional","affiliation":[]},{"given":"Kannappan","family":"Palaniappan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,24]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 1\u201313 (2017)","journal-title":"Sci. Data"},{"key":"1_CR2","unstructured":"Bakas, S., et\u00a0al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Bao, R., et\u00a0al.: BOston neonatal brain injury dataset for hypoxic ischemic encephalopathy (BONBID-HIE): Part I. MRI and Manual Lesion Annotation. bioRxiv, pp. 2023\u201306 (2023)","DOI":"10.1101\/2023.06.30.546841"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Birsan, T., Tiba, D.: One hundred years since the introduction of the set distance by dimitrie pompeiu. In: System Modeling and Optimization, pp. 35\u201339 (2006)","DOI":"10.1007\/0-387-33006-2_4"},{"key":"1_CR5","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR (2021)"},{"issue":"1","key":"1_CR6","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1001\/jamapediatrics.2019.4011","volume":"174","author":"M Finder","year":"2020","unstructured":"Finder, M., Boylan, G.B., Twomey, D., Ahearne, C., Murray, D.M., Hallberg, B.: Two-year neurodevelopmental outcomes after mild hypoxic ischemic encephalopathy in the era of therapeutic hypothermia. JAMA Pediatr. 174(1), 48\u201355 (2020)","journal-title":"JAMA Pediatr."},{"issue":"6","key":"1_CR7","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1016\/j.ajog.2008.06.094","volume":"199","author":"EM Graham","year":"2008","unstructured":"Graham, E.M., Ruis, K.A., Hartman, A.L., Northington, F.J., Fox, H.E.: A systematic review of the role of intrapartum hypoxia-ischemia in the causation of neonatal encephalopathy. Am. J. Obstetr. Gynecol. 199(6), 587\u2013595 (2008)","journal-title":"Am. J. Obstetr. Gynecol."},{"key":"1_CR8","doi-asserted-by":"publisher","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. In: Crimi, A., Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, BrainLes 2021, LNCS, vol. 12962, pp. 272\u2013284 Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22","DOI":"10.1007\/978-3-031-08999-2_22"},{"issue":"9","key":"1_CR9","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1109\/34.232073","volume":"15","author":"D Huttenlocher","year":"1993","unstructured":"Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing images using the hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850\u2013863 (1993)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"1_CR10","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1109\/TMI.2019.2930068","volume":"39","author":"D Karimi","year":"2019","unstructured":"Karimi, D., Salcudean, S.E.: Reducing the hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Trans. Med. Imaging 39(2), 499\u2013513 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1_CR11","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NeurIPS (2012)"},{"issue":"1","key":"1_CR12","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1038\/pr.2013.206","volume":"74","author":"AC Lee","year":"2013","unstructured":"Lee, A.C., et al.: Intrapartum-related neonatal encephalopathy incidence and impairment at regional and global levels for 2010 with trends from 1990. Pediatr. Res. 74(1), 50\u201372 (2013)","journal-title":"Pediatr. Res."},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: IEEE CVPR. pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1_CR14","unstructured":"Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. NeurIPS (2016)"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Lyu, L., Toubal, I.E., Palaniappan, K.: Multi-expert deep networks for multi-disease detection in retinal fundus images. In: Int. Conf. IEEE Engineering in Medicine & Biology Society (EMBC). pp. 1818\u20131822 (2022)","DOI":"10.1109\/EMBC48229.2022.9871762"},{"key":"1_CR16","unstructured":"Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et\u00a0al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Transactions on Medical Imaging pp. 1993\u20132024 (2014)"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Murphy, K., van\u00a0der Aa, N.E., Negro, S., Groenendaal, F., de\u00a0Vries, L.S., Viergever, M.A., Boylan, G.B., Benders, M.J., I\u0161gum, I.: Automatic quantification of ischemic injury on diffusion-weighted mri of neonatal hypoxic ischemic encephalopathy. NeuroImage: Clinical pp. 222\u2013232 (2017)","DOI":"10.1016\/j.nicl.2017.01.005"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Rahmon, G., Palaniappan, K., Toubal, I.E., Bunyak, F., Rao, R., Seetharaman, G.: Deepftsg: Multi-stream asymmetric use-net trellis encoders with shared decoder feature fusion architecture for video motion segmentation. International Journal of Computer Vision pp. 1\u201329 (2023)","DOI":"10.1007\/s11263-023-01910-x"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Rahmon, G., Toubal, I.E., Palaniappan, K.: Extending u-net network for improved nuclei instance segmentation accuracy in histopathology images. In: IEEE Applied Imagery Pattern Recognition Workshop (AIPR). pp.\u00a01\u20137 (2021)","DOI":"10.1109\/AIPR52630.2021.9762213"},{"key":"1_CR20","unstructured":"Reinke, A., Tizabi, M.D., Sudre, C.H., Eisenmann, M., R\u00e4dsch, T., Baumgartner, M., Acion, L., Antonelli, M., Arbel, T., Bakas, S., et\u00a0al.: Common limitations of image processing metrics: A picture story. arXiv preprint arXiv:2104.05642 (2021)"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI. pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Rutherford, M., Malamateniou, C., McGuinness, A., Allsop, J., Biarge, M.M., Counsell, S.: Magnetic resonance imaging in hypoxic-ischaemic encephalopathy. Early human development pp. 351\u2013360 (2010)","DOI":"10.1016\/j.earlhumdev.2010.05.014"},{"key":"1_CR23","unstructured":"Shankaran, S., Barnes, P.D., Hintz, S.R., Laptook, A.R., Zaterka-Baxter, K.M., McDonald, S.A., Ehrenkranz, R.A., Walsh, M.C., Tyson, J.E., Donovan, E.F., et\u00a0al.: Brain injury following trial of hypothermia for neonatal hypoxic\u2013ischaemic encephalopathy. Archives of Disease in Childhood-Fetal and Neonatal Edition pp. F398\u2013F404 (2012)"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge\u00a0Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: MICCAI. pp. 240\u2013248 (2017)","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Toubal, I.E., Al-Shakarji, N., Cornelison, D., Palaniappan, K.: Ensemble deep learning object detection fusion for cell tracking, mitosis, and lineage. IEEE Open Journal of Engineering in Medicine and Biology (2023)","DOI":"10.1109\/OJEMB.2023.3288470"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Toubal, I.E., Duan, Y., Yang, D.: Deep learning semantic segmentation for high-resolution medical volumes. In: IEEE Applied Imagery Pattern Recognition Workshop (AIPR). pp.\u00a01\u20139 (2020)","DOI":"10.1109\/AIPR50011.2020.9425041"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Toubal, I.E., Lyu, L., Lin, D., Palaniappan, K.: Single view facial age estimation using deep learning with cascaded random forests. In: Computer Analysis of Images and Patterns (CAIP). pp. 285\u2013296 (2021)","DOI":"10.1007\/978-3-030-89131-2_26"},{"key":"1_CR28","unstructured":"Wang, X., Kondratyuk, D., Christiansen, E., Kitani, K.M., Alon, Y., Eban, E.: Wisdom of committees: An overlooked approach to faster and more accurate models (2022)"},{"key":"1_CR29","doi-asserted-by":"crossref","unstructured":"Weeke, L.C., Groenendaal, F., Mudigonda, K., Blennow, M., Lequin, M.H., Meiners, L.C., van Haastert, I.C., Benders, M.J., Hallberg, B., de\u00a0Vries, L.S.: A novel magnetic resonance imaging score predicts neurodevelopmental outcome after perinatal asphyxia and therapeutic hypothermia. The Journal of Pediatrics pp. 33\u201340 (2018)","DOI":"10.1016\/j.jpeds.2017.09.043"}],"container-title":["Lecture Notes in Computer Science","AI for Brain Lesion Detection and Trauma Video Action Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-71626-3_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T16:03:00Z","timestamp":1729699380000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-71626-3_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,24]]},"ISBN":["9783031716256","9783031716263"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-71626-3_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,24]]},"assertion":[{"value":"24 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"BONBID-HIE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lesion Segmentation Challenge - Boston Neonatal Brain Injury Dataset for Hypoxic Ischemic Encephalopathy","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bonbidhie2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bonbid-hie2023.grand-challenge.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}