{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T22:58:59Z","timestamp":1781218739472,"version":"3.54.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030720865","type":"print"},{"value":"9783030720872","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-72087-2_8","type":"book-chapter","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T04:09:34Z","timestamp":1616645374000},"page":"81-92","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Attention U-Net with Dimension-Hybridized Fast Data Density Functional Theory for Automatic Brain Tumor Image Segmentation"],"prefix":"10.1007","author":[{"given":"Zi-Jun","family":"Su","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tang-Chen","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yen-Ling","family":"Tai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu-Jung","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2058-1263","authenticated-orcid":false,"given":"Chien-Chang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,3,26]]},"reference":[{"key":"8_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-030-11726-9_28","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"A Myronenko","year":"2019","unstructured":"Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311\u2013320. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_28"},{"key":"8_CR2","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1109\/TMI.2018.2805821","volume":"37","author":"C Ma","year":"2018","unstructured":"Ma, C., Luo, G., Wang, K.: Concatenated and connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images. IEEE Trans. Med. Imaging 37, 1943\u20131954 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR3","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neuroimage.2014.12.061","volume":"108","author":"W Zhang","year":"2015","unstructured":"Zhang, W., et al.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Neuroimage 108, 214\u2013224 (2015)","journal-title":"Neuroimage"},{"key":"8_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ultras.2018.07.006","volume":"91","author":"Y Xu","year":"2019","unstructured":"Xu, Y., Wang, Y., Yuan, J., Cheng, Q., Wang, X., Carson, P.L.: Medical breast ultrasound image segmentation by machine learning. Ultrasonics 91, 1\u20139 (2019)","journal-title":"Ultrasonics"},{"key":"8_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2014.10.012","volume":"20","author":"E Smistad","year":"2015","unstructured":"Smistad, E., Falch, T.L., Bozorgi, M., Elster, A.C., Lindseth, F.: Medical image segmentation on GPUs\u2013a comprehensive review. Med. Image Anal. 20, 1\u20138 (2015)","journal-title":"Med. Image Anal."},{"key":"8_CR6","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":"8_CR7","doi-asserted-by":"crossref","unstructured":"Dasgupta, A., Singh, S.: A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 248\u2013251. IEEE (2017)","DOI":"10.1109\/ISBI.2017.7950512"},{"key":"8_CR8","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":"8_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1007\/978-3-319-75238-9_38","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"K Kamnitsas","year":"2018","unstructured":"Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 450\u2013462. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_38"},{"key":"8_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jvcir.2016.11.019","volume":"43","author":"A Pratondo","year":"2017","unstructured":"Pratondo, A., Chui, C.-K., Ong, S.-H.: Integrating machine learning with region-based active contour models in medical image segmentation. J. Vis. Commun. Image Represent. 43, 1\u20139 (2017)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"8_CR11","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.1109\/TIP.2011.2146190","volume":"20","author":"C Li","year":"2011","unstructured":"Li, C., Huang, R., Ding, Z., Gatenby, J.C., Metaxas, D.N., Gore, J.C.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20, 2007\u20132016 (2011)","journal-title":"IEEE Trans. Image Process."},{"key":"8_CR12","doi-asserted-by":"publisher","first-page":"1718","DOI":"10.3390\/app9081718","volume":"9","author":"C-C Chen","year":"2019","unstructured":"Chen, C.-C., Tsai, M.-Y., Kao, M.-Z., Lu, H.H.-S.: Medical image segmentation with adjustable computational complexity using data density functionals. Appl. Sci. 9, 1718 (2019)","journal-title":"Appl. Sci."},{"key":"8_CR13","first-page":"1","volume":"8","author":"C-C Chen","year":"2018","unstructured":"Chen, C.-C., Juan, H.-H., Tsai, M.-Y., Lu, H.H.-S.: Unsupervised learning and pattern recognition of biological data structures with density functional theory and machine learning. Sci. Rep. 8, 1\u201311 (2018)","journal-title":"Sci. Rep."},{"key":"8_CR14","unstructured":"Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:.02629 (2018)"},{"key":"8_CR15","doi-asserted-by":"publisher","first-page":"170117","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, 170117 (2017)","journal-title":"Sci. Data"},{"key":"8_CR16","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34, 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR17","doi-asserted-by":"publisher","first-page":"170117","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The cancer imaging archive. Nat. Sci. Data 4, 170117 (2017)","journal-title":"Nat. Sci. Data"},{"key":"8_CR18","unstructured":"Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. 286 (2017)"},{"key":"8_CR19","series-title":"Springer Handbooks of Computational Statistics","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/978-3-319-18284-1_15","volume-title":"Handbook of Big Data Analytics","author":"C-C Chen","year":"2018","unstructured":"Chen, C.-C., Juan, H.-H., Tsai, M.-Y., Lu, H.-S.: Bridging density functional theory and big data analytics with applications. In: H\u00e4rdle, W.K., Lu, H.-S., Shen, X. (eds.) Handbook of Big Data Analytics. SHCS, pp. 351\u2013374. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-18284-1_15"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156\u20133164 (2015)","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"8_CR21","unstructured":"Oktay, O., et al.: Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:.03999 (2018)"},{"key":"8_CR22","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/42.836373","volume":"19","author":"LG Ny\u00fal","year":"2000","unstructured":"Ny\u00fal, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19, 143\u2013150 (2000)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Garc\u00eda, F., Sparks, R., Ourselin, S.: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. arXiv preprint arXiv:.04696 (2020)","DOI":"10.1016\/j.cmpb.2021.106236"},{"key":"8_CR24","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.1103\/PhysRevB.28.1809","volume":"28","author":"DC Langreth","year":"1983","unstructured":"Langreth, D.C., Mehl, M.: Beyond the local-density approximation in calculations of ground-state electronic properties. Phys. Rev. B 28, 1809 (1983)","journal-title":"Phys. Rev. B"},{"key":"8_CR25","doi-asserted-by":"publisher","first-page":"174120","DOI":"10.1103\/PhysRevB.92.174120","volume":"92","author":"M Zaiser","year":"2015","unstructured":"Zaiser, M.: Local density approximation for the energy functional of three-dimensional dislocation systems. Phys. Rev. B 92, 174120 (2015)","journal-title":"Phys. Rev. B"},{"key":"8_CR26","doi-asserted-by":"crossref","unstructured":"Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C.: Efficient object localization using convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 648\u2013656 (2015)","DOI":"10.1109\/CVPR.2015.7298664"}],"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\/978-3-030-72087-2_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T01:03:02Z","timestamp":1774400582000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72087-2_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030720865","9783030720872"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72087-2_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"26 March 2021","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":"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":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2020","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}