{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T15:42:12Z","timestamp":1769010132406,"version":"3.49.0"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030139681","type":"print"},{"value":"9783030139698","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-13969-8_4","type":"book-chapter","created":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T11:04:13Z","timestamp":1568891053000},"page":"69-91","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples"],"prefix":"10.1007","author":[{"given":"Yingwei","family":"Li","sequence":"first","affiliation":[]},{"given":"Zhuotun","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yuyin","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yingda","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Elliot K.","family":"Fishman","sequence":"additional","affiliation":[]},{"given":"Alan L.","family":"Yuille","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,20]]},"reference":[{"key":"4_CR1","unstructured":"Bui TD, Shin J, Moon T (2017) 3D densely convolution networks for volumetric segmentation. arXiv:1709.03199"},{"key":"4_CR2","unstructured":"Cai J, Lu L, Xie Y, Xing F, Yang L (2017) Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function"},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Chen H, Dou Q, Yu L, Qin J, Heng PA (2017) Voxresnet: deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage","DOI":"10.1016\/j.neuroimage.2017.04.041"},{"key":"4_CR4","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2016) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. arXiv:1606.00915"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D u-net: learning dense volumetric segmentation from sparse annotation. In: MICCAI","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"4_CR6","first-page":"40","volume":"41","author":"Q Dou","year":"2017","unstructured":"Dou Q, Yu L, Chen H, Jin Y, Yang X, Qin J, Heng PA (2017) 3D deeply supervised network for automated segmentation of volumetric medical images. MIA 41:40\u201354","journal-title":"MIA"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Fang Y, Xie J, Dai G, Wang M, Zhu F, Xu T, Wong E (2015) 3D deep shape descriptor. In: CVPR","DOI":"10.1109\/CVPR.2015.7298845"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC (2018) Automatic multi-organ segmentation on abdominal ct with dense v-networks. TMI","DOI":"10.1109\/TMI.2018.2806309"},{"key":"4_CR9","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: AISTATS"},{"key":"4_CR10","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2015) Explaining and harnessing adversarial examples. In: ICLR"},{"issue":"10","key":"4_CR11","first-page":"1221","volume":"23","author":"P Gravel","year":"2004","unstructured":"Gravel P, Beaudoin G, De Guise JA (2004) A method for modeling noise in medical images. TMI 23(10):1221\u20131232","journal-title":"TMI"},{"key":"4_CR12","first-page":"18","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei M, Davy A, Warde-Farley D, Biard A, Courville AC, Bengio Y, Pal C, Jodoin P, Larochelle H (2017) Brain tumor segmentation with deep neural networks. MIA 35:18\u201331","journal-title":"MIA"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR","DOI":"10.1109\/CVPR.2016.90"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Huang Y, W\u00fcrfl T, Breininger K, Liu L, Lauritsch G, Maier A (2018) Some investigations on robustness of deep learning in limited angle tomography. In: MICCAI","DOI":"10.1007\/978-3-030-00928-1_17"},{"key":"4_CR15","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: ICML"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) CAFFE: convolutional architecture for fast feature embedding. MM","DOI":"10.1145\/2647868.2654889"},{"key":"4_CR17","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: NIPS"},{"key":"4_CR18","unstructured":"Kurakin A, Goodfellow I, Bengio S (2017) Adversarial machine learning at scale. In: ICLR"},{"issue":"3","key":"4_CR19","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.acra.2009.09.013","volume":"17","author":"AA Lasboo","year":"2010","unstructured":"Lasboo AA, Rezai P, Yaghmai V (2010) Morphological analysis of pancreatic cystic masses. Acad Radiol 17(3):348\u2013351","journal-title":"Acad Radiol"},{"key":"4_CR20","unstructured":"Lee CY, Xie S, Gallagher P, Zhang Z, Tu Z (2015) Deeply-supervised nets. In: AISTATS"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: CVPR","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"4_CR22","unstructured":"Madry A, Makelov A, Schmidt L, Tsipras D, Vladu, A (2018) Towards deep learning models resistant to adversarial attacks. In: ICLR"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Merkow J, Marsden A, Kriegman D, Tu Z (2016) Dense volume-to-volume vascular boundary detection. In: MICCAI","DOI":"10.1007\/978-3-319-46726-9_43"},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 3DV","DOI":"10.1109\/3DV.2016.79"},{"key":"4_CR25","unstructured":"Moeskops P, Wolterink JM, van der Velden BHM, Gilhuijs KGA, Leiner T, Viergever MA, Isgum I (2017) Deep learning for multi-task medical image segmentation in multiple modalities. CoRR arXiv:1704.03379"},{"key":"4_CR26","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: ICML"},{"key":"4_CR27","unstructured":"Paschali M, Conjeti S, Navarro F, Navab N (2018) Generalizability versus robustness: adversarial examples for medical imaging. In: MICCAI"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"4_CR29","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: MICCAI","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"4_CR30","unstructured":"Roth H, Oda M, Shimizu N, Oda H, Hayashi Y, Kitasaka T, Fujiwara M, Misawa K, Mori K (2018) Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks. In: SPIE"},{"key":"4_CR31","doi-asserted-by":"crossref","unstructured":"Roth HR, Lu L, Farag A, Shin HC, Liu J, Turkbey EB, Summers RM (2015) Deeporgan: multi-level deep convolutional networks for automated pancreas segmentation. In: MICCAI","DOI":"10.1117\/12.2081420"},{"key":"4_CR32","doi-asserted-by":"crossref","unstructured":"Roth HR, Lu L, Farag A, Sohn A, Summers RM (2016) Spatial aggregation of holistically-nested networks for automated pancreas segmentation. In: MICCAI","DOI":"10.1007\/978-3-319-46723-8_52"},{"key":"4_CR33","doi-asserted-by":"crossref","unstructured":"Shen W, Wang B, Jiang Y, Wang Y, Yuille AL (2017) Multi-stage multi-recursive-input fully convolutional networks for neuronal boundary detection. In: ICCV, pp 2410\u20132419","DOI":"10.1109\/ICCV.2017.262"},{"key":"4_CR34","unstructured":"Shen W, Wang X, Wang Y, Bai X, Zhang Z (2015) Deepcontour: a deep convolutional feature learned by positive-sharing loss for contour detection. In: CVPR"},{"key":"4_CR35","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"4_CR36","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: CVPR","DOI":"10.1109\/CVPR.2016.308"},{"key":"4_CR37","unstructured":"Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2014) Intriguing properties of neural networks. In: ICLR"},{"key":"4_CR38","unstructured":"Tsipras D, Santurkar S, Engstrom L, Turner A, Madry A (2018) Robustness may be at odds with accuracy, p 1. arXiv:1805.12152"},{"key":"4_CR39","unstructured":"Wang Y, Zhou Y, Shen W, Park S, Fishman EK, Yuille AL (2018) Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. CoRR. arXiv:1804.08414"},{"key":"4_CR40","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1007\/978-3-030-00937-3_50","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Yan Wang","year":"2018","unstructured":"Wang Y, Zhou Y, Tang P, Shen W, Fishman EK, Yuille AL (2018) Training multi-organ segmentation networks with sample selection by relaxed upper confident bound. In: Proceedings of MICCAI, pp. 434\u2013442"},{"key":"4_CR41","doi-asserted-by":"crossref","unstructured":"Xie C, Wang J, Zhang Z, Zhou Y, Xie L, Yuille A (2017) Adversarial examples for semantic segmentation and object detection. In: ICCV","DOI":"10.1109\/ICCV.2017.153"},{"key":"4_CR42","doi-asserted-by":"crossref","unstructured":"Xie S, Tu Z (2015) Holistically-nested edge detection. In: ICCV","DOI":"10.1109\/ICCV.2015.164"},{"key":"4_CR43","doi-asserted-by":"crossref","unstructured":"Yu L, Cheng JZ, Dou Q, Yang X, Chen H, Qin J, Heng PA (2017) Automatic 3D cardiovascular MR segmentation with densely-connected volumetric convnets. In: MICCAI","DOI":"10.1007\/978-3-319-66185-8_33"},{"key":"4_CR44","doi-asserted-by":"crossref","unstructured":"Yu L, Yang X, Chen H, Qin J, Heng P (2017) Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images. In: AAAI","DOI":"10.1609\/aaai.v31i1.10510"},{"key":"4_CR45","doi-asserted-by":"crossref","unstructured":"Zhou Y, Xie L, Fishman EK, Yuille AL (2017) Deep supervision for pancreatic cyst segmentation in abdominal CT scans. In: MICCAI","DOI":"10.1007\/978-3-319-66179-7_26"},{"key":"4_CR46","doi-asserted-by":"crossref","unstructured":"Zhou Y, Xie L, Shen W, Wang Y, Fishman EK, Yuille AL (2017) A fixed-point model for pancreas segmentation in abdominal CT scans. In: MICCAI","DOI":"10.1007\/978-3-319-66182-7_79"},{"key":"4_CR47","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.neucom.2015.08.127","volume":"204","author":"Z Zhu","year":"2016","unstructured":"Zhu Z, Wang X, Bai S, Yao C, Bai X (2016) Deep learning representation using autoencoder for 3D shape retrieval. Neurocomputing 204:41\u201350","journal-title":"Neurocomputing"},{"key":"4_CR48","doi-asserted-by":"crossref","unstructured":"Zhu Z, Xia Y, Shen W, Fishman EK, Yuille AL (2018) A 3d coarse-to-fine framework for volumetric medical image segmentation. In: International conference on 3D vision, pp 682\u2013690","DOI":"10.1109\/3DV.2018.00083"},{"key":"4_CR49","unstructured":"Zhu Z, Xia Y, Xie L, Fishman EK, Yuille AL (2018) Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma. arXiv:1807.02941"}],"container-title":["Advances in Computer Vision and Pattern Recognition","Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-13969-8_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T01:30:20Z","timestamp":1664415020000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-13969-8_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030139681","9783030139698"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-13969-8_4","relation":{},"ISSN":["2191-6586","2191-6594"],"issn-type":[{"value":"2191-6586","type":"print"},{"value":"2191-6594","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"20 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}