乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T04:06:57Z","timestamp":1745294817484},"reference-count":78,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2019,11]]},"DOI":"10.1016\/j.neucom.2019.07.006","type":"journal-article","created":{"date-parts":[[2019,7,23]],"date-time":"2019-07-23T01:59:25Z","timestamp":1563847165000},"page":"31-43","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":220,"special_numbering":"C","title":["USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets"],"prefix":"10.1016","volume":"365","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-3341-5483","authenticated-orcid":false,"given":"Leonardo","family":"Rundo","sequence":"first","affiliation":[]},{"given":"Changhee","family":"Han","sequence":"additional","affiliation":[]},{"given":"Yudai","family":"Nagano","sequence":"additional","affiliation":[]},{"given":"Jin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ryuichiro","family":"Hataya","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2249-9538","authenticated-orcid":false,"given":"Carmelo","family":"Militello","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Tangherloni","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7692-7203","authenticated-orcid":false,"given":"Marco S.","family":"Nobile","sequence":"additional","affiliation":[]},{"given":"Claudio","family":"Ferretti","sequence":"additional","affiliation":[]},{"given":"Daniela","family":"Besozzi","sequence":"additional","affiliation":[]},{"given":"Maria Carla","family":"Gilardi","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2673-8551","authenticated-orcid":false,"given":"Salvatore","family":"Vitabile","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3520-4022","authenticated-orcid":false,"given":"Giancarlo","family":"Mauri","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8726-2780","authenticated-orcid":false,"given":"Hideki","family":"Nakayama","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7780-0434","authenticated-orcid":false,"given":"Paolo","family":"Cazzaniga","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.neucom.2019.07.006_bib0001","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21551","article-title":"Cancer statistics, 2019","volume":"69","author":"Siegel","year":"2019","journal-title":"CA Cancer J. Clin."},{"key":"10.1016\/j.neucom.2019.07.006_bib0002","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compbiomed.2015.02.009","article-title":"Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review","volume":"60","author":"Lema\u00eetre","year":"2015","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"10.1016\/j.neucom.2019.07.006_bib0003","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1007\/s00330-005-2893-8","article-title":"Prostate MR imaging at high-field strength: evolution or revolution?","volume":"16","author":"Rouvi\u00e8re","year":"2006","journal-title":"Eur. Radiol."},{"issue":"2","key":"10.1016\/j.neucom.2019.07.006_bib0004","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1097\/RCT.0b013e3180683b99","article-title":"Update of prostate magnetic resonance imaging at 3 T","volume":"32","author":"Kim","year":"2008","journal-title":"J. Comput. Assist. Tomogr."},{"issue":"1","key":"10.1016\/j.neucom.2019.07.006_bib0005","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1148\/radiol.2441060425","article-title":"Prostate cancer: body-array versus endorectal coil MR imaging at 3T\u2014comparison of image quality, localization, and staging performance","volume":"244","author":"Heijmink","year":"2007","journal-title":"Radiology"},{"issue":"2\u20133","key":"10.1016\/j.neucom.2019.07.006_bib0006","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.euf.2016.06.018","article-title":"Rationale for modernising imaging in advanced prostate cancer","volume":"3","author":"Padhani","year":"2017","journal-title":"Eur. Urol. Focus"},{"issue":"3","key":"10.1016\/j.neucom.2019.07.006_bib0007","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.ejrad.2007.06.030","article-title":"Magnetic resonance imaging (MRI) anatomy of the prostate and application of MRI in radiotherapy planning","volume":"63","author":"Villeirs","year":"2007","journal-title":"Eur. J. Radiol."},{"issue":"6","key":"10.1016\/j.neucom.2019.07.006_bib0008","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1016\/j.urology.2011.07.1395","article-title":"The mcneal prostate: a review","volume":"78","author":"Selman","year":"2011","journal-title":"Urology"},{"issue":"9","key":"10.1016\/j.neucom.2019.07.006_bib0009","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1097\/RLI.0000000000000163","article-title":"Multiparametric magnetic resonance imaging in prostate cancer management: current status and future perspectives","volume":"50","author":"Scheenen","year":"2015","journal-title":"Invest. Radiol."},{"issue":"1","key":"10.1016\/j.neucom.2019.07.006_bib0010","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1148\/radiol.11091822","article-title":"Prostate cancer: multiparametric MR imaging for detection, localization, and staging","volume":"261","author":"Hoeks","year":"2011","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.neucom.2019.07.006_bib0011","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1148\/rg.271065078","article-title":"Functional MR imaging of prostate cancer","volume":"27","author":"Choi","year":"2007","journal-title":"Radiographics"},{"issue":"12","key":"10.1016\/j.neucom.2019.07.006_bib0012","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1088\/0031-9155\/57\/12\/3833","article-title":"Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI","volume":"57","author":"Niaf","year":"2012","journal-title":"Phys. Med. Biol."},{"issue":"3","key":"10.1016\/j.neucom.2019.07.006_bib0013","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1002\/pros.20881","article-title":"Peripheral zone prostate cancers: location and intraprostatic patterns of spread at histopathology","volume":"69","author":"Haffner","year":"2009","journal-title":"Prostate"},{"issue":"21","key":"10.1016\/j.neucom.2019.07.006_bib0014","doi-asserted-by":"crossref","first-page":"34836","DOI":"10.18632\/oncotarget.16753","article-title":"Peripheral zone volume ratio (PZ-ratio) is relevant with biopsy results and can increase the accuracy of current diagnostic modality","volume":"8","author":"Chang","year":"2017","journal-title":"Oncotarget"},{"key":"10.1016\/j.neucom.2019.07.006_bib0015","series-title":"Fast Facts: Benign Prostatic Hyperplasia","author":"Kirby","year":"2011"},{"issue":"1","key":"10.1016\/j.neucom.2019.07.006_bib0016","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.eururo.2015.08.052","article-title":"PI-RADS Prostate imaging\u2013reporting and data system: 2015, version 2","volume":"69","author":"Weinreb","year":"2016","journal-title":"Eur. Urol."},{"issue":"2","key":"10.1016\/j.neucom.2019.07.006_bib0017","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3390\/info8020049","article-title":"Automated prostate gland segmentation based on an unsupervised fuzzy c-means clustering technique using multispectral T1w and T2w MR imaging","volume":"8","author":"Rundo","year":"2017","journal-title":"Information"},{"issue":"3","key":"10.1016\/j.neucom.2019.07.006_bib0018","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1148\/radiol.2015142818","article-title":"Prostate cancer: interobserver agreement and accuracy with the revised prostate imaging reporting and data system at multiparametric MR imaging","volume":"277","author":"Muller","year":"2015","journal-title":"Radiology"},{"issue":"4","key":"10.1016\/j.neucom.2019.07.006_bib0019","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1118\/1.2842076","article-title":"Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information","volume":"35","author":"Klein","year":"2008","journal-title":"Med. Phys."},{"issue":"1","key":"10.1016\/j.neucom.2019.07.006_bib0020","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.media.2015.06.010","article-title":"Weighting training images by maximizing distribution similarity for supervised segmentation across scanners","volume":"24","author":"van Opbroek","year":"2015","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2019.07.006_sbref0021","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2019.07.006_bib0022","series-title":"Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2818","article-title":"Rethinking the Inception architecture for computer vision","author":"Szegedy","year":"2016"},{"key":"10.1016\/j.neucom.2019.07.006_bib0023","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"2","key":"10.1016\/j.neucom.2019.07.006_bib0024","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1073\/pnas.1715832114","article-title":"A mixed-scale dense convolutional neural network for image analysis","volume":"115","author":"Pelt","year":"2018","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"4","key":"10.1016\/j.neucom.2019.07.006_bib0025","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/j.media.2014.02.009","article-title":"Dual optimization based prostate zonal segmentation in 3D MR images","volume":"18","author":"Qiu","year":"2014","journal-title":"Med. Image Anal."},{"issue":"6","key":"10.1016\/j.neucom.2019.07.006_bib0026","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/s11548-008-0247-0","article-title":"Atlas-based prostate segmentation using an hybrid registration","volume":"3","author":"Martin","year":"2008","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"4","key":"10.1016\/j.neucom.2019.07.006_bib0027","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1118\/1.3315367","article-title":"Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model","volume":"37","author":"Martin","year":"2010","journal-title":"Med. Phys."},{"issue":"9","key":"10.1016\/j.neucom.2019.07.006_bib0028","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1016\/j.cviu.2012.11.013","article-title":"Simultaneous segmentation of prostatic zones using active appearance models with multiple coupled levelsets","volume":"117","author":"Toth","year":"2013","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.neucom.2019.07.006_bib0029","series-title":"Multidisciplinary Approaches to Neural Computing","first-page":"23","article-title":"Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy c-means clustering algorithm","volume":"69","author":"Rundo","year":"2018"},{"key":"10.1016\/j.neucom.2019.07.006_bib0030","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"issue":"4","key":"10.1016\/j.neucom.2019.07.006_bib0031","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1109\/TMI.2015.2508280","article-title":"Deformable MR prostate segmentation via deep feature learning and sparse patch matching","volume":"35","author":"Guo","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2019.07.006_bib0032","series-title":"Proceddings of the International Workshop on Machine Learning in Medical Imaging","first-page":"220","article-title":"A point says a lot: An interactive segmentation method for MR prostate via one-point labeling","author":"Sun","year":"2017"},{"key":"10.1016\/j.neucom.2019.07.006_bib0033","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1016\/j.neucom.2017.09.084","article-title":"Atlas registration and ensemble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging","volume":"275","author":"Jia","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2019.07.006_bib0034","series-title":"Proceedings of the International Conference on 3D Vision (3DV)","first-page":"565","article-title":"V-Net: fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"10.1016\/j.neucom.2019.07.006_bib0035","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.media.2017.08.006","article-title":"Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI","volume":"42","author":"Yang","year":"2017","journal-title":"Med. Image Anal."},{"issue":"5","key":"10.1016\/j.neucom.2019.07.006_bib0036","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1109\/TMI.2017.2789181","article-title":"Automated detection of clinically significant prostate cancer in mp-MRI images based on an end-to-end deep neural network","volume":"37","author":"Wang","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2019.07.006_bib0037","series-title":"Proceedings of the International Symposium on Biomedical Imaging (ISBI): Nano to Macro","first-page":"410","article-title":"Differential segmentation of the prostate in MR images using combined 3D shape modelling and voxel classification","author":"Allen","year":"2006"},{"key":"10.1016\/j.neucom.2019.07.006_bib0038","series-title":"Medical Imaging: Image Processing","first-page":"83143B","article-title":"Fully automated 3D prostate central gland segmentation in MR images: a LOGISMOS based approach","volume":"8314","author":"Yin","year":"2012"},{"key":"10.1016\/j.neucom.2019.07.006_bib0039","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2217","article-title":"A study on continuous max-flow and min-cut approaches","author":"Yuan","year":"2010"},{"issue":"11","key":"10.1016\/j.neucom.2019.07.006_bib0040","doi-asserted-by":"crossref","first-page":"6093","DOI":"10.1118\/1.3651610","article-title":"Zonal segmentation of prostate using multispectral magnetic resonance images","volume":"38","author":"Makni","year":"2011","journal-title":"Med. Phys."},{"issue":"4","key":"10.1016\/j.neucom.2019.07.006_bib0041","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1016\/j.patcog.2007.08.014","article-title":"ECM: An evidential version of the fuzzy c-means algorithm","volume":"41","author":"Masson","year":"2008","journal-title":"Pattern Recognit."},{"issue":"4","key":"10.1016\/j.neucom.2019.07.006_bib0042","doi-asserted-by":"crossref","first-page":"041307","DOI":"10.1117\/1.JMI.4.4.041307","article-title":"Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks","volume":"4","author":"Clark","year":"2017","journal-title":"J. Med. Imaging"},{"key":"10.1016\/j.neucom.2019.07.006_sbref0043","series-title":"Proceedings of the International Conference on Learning Representations (ICLR)","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2015"},{"key":"10.1016\/j.neucom.2019.07.006_bib0044","series-title":"Proceedings of the Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","volume":"9351","author":"Ronneberger","year":"2015"},{"issue":"1","key":"10.1016\/j.neucom.2019.07.006_bib0045","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1002\/ima.22253","article-title":"NeXt for neuro-radiosurgery: a fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique","volume":"28","author":"Rundo","year":"2018","journal-title":"Int. J. Imaging Syst. Technol."},{"issue":"2","key":"10.1016\/j.neucom.2019.07.006_bib0046","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/TMI.2016.2621185","article-title":"Deepcut: object segmentation from bounding box annotations using convolutional neural networks","volume":"36","author":"Rajchl","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"10.1016\/j.neucom.2019.07.006_bib0047","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s11047-017-9636-z","article-title":"GTVcut for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model","volume":"17","author":"Rundo","year":"2018","journal-title":"Nat. Comput."},{"key":"10.1016\/j.neucom.2019.07.006_bib0048","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","article-title":"Brain tumor segmentation with deep neural networks","volume":"35","author":"Havaei","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2019.07.006_bib0049","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2019.07.006_bib0050","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","article-title":"Attention gated networks: learning to leverage salient regions in medical images","volume":"53","author":"Schlemper","year":"2019","journal-title":"Med. Image Anal."},{"issue":"12","key":"10.1016\/j.neucom.2019.07.006_bib0051","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SEGNET: a deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2019.07.006_bib0052","doi-asserted-by":"crossref","unstructured":"P. Isola, J.-Y. Zhu, T. Zhou, A.A. Efros, Image-to-image translation with conditional adversarial networks, arXiv:1611.07004, (2016).","DOI":"10.1109\/CVPR.2017.632"},{"issue":"3","key":"10.1016\/j.neucom.2019.07.006_bib0053","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1002\/mp.12752","article-title":"Deep learning for segmentation of brain tumors: impact of cross-institutional training and testing","volume":"45","author":"AlBadawy","year":"2018","journal-title":"Med. Phys."},{"key":"10.1016\/j.neucom.2019.07.006_bib0054","series-title":"Neural Approaches to Dynamics of Signal Exchanges","article-title":"CNN-based prostate zonal segmentation on T2-weighted MR images: a cross-dataset study","volume":"151","author":"Rundo","year":"2020"},{"key":"10.1016\/j.neucom.2019.07.006_bib0055","doi-asserted-by":"crossref","first-page":"170124","DOI":"10.1038\/sdata.2017.124","article-title":"The public cancer radiology imaging collections of the cancer imaging archive","volume":"4","author":"Prior","year":"2017","journal-title":"Sci. Data"},{"key":"#cr-split#-10.1016\/j.neucom.2019.07.006_bib0056.1","unstructured":"N. Bloch, A. Madabhushi, H. Huisman, J. Freymann, J. Kirby, M. Grauer, et\u00a0al., NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures. The Cancer Imaging Archive, 2015Online"},{"key":"#cr-split#-10.1016\/j.neucom.2019.07.006_bib0056.2","unstructured":"Accessed on July 30, 2018. doi:10.7937\/K9\/TCIA.2015.zF0vlOPv."},{"issue":"9","key":"10.1016\/j.neucom.2019.07.006_bib0057","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.1109\/TBME.2018.2828137","article-title":"Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation","volume":"65","author":"Yan","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.neucom.2019.07.006_bib0058","series-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)","first-page":"497","article-title":"Generalizing deep models for ultrasound image segmentation","author":"Yang","year":"2018"},{"issue":"1","key":"10.1016\/j.neucom.2019.07.006_bib0059","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1016\/j.eswa.2006.10.016","article-title":"Visualization and analysis of classifiers performance in multi-class medical data","volume":"34","author":"Diri","year":"2008","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"10.1016\/j.neucom.2019.07.006_bib0060","doi-asserted-by":"crossref","first-page":"3513","DOI":"10.1016\/j.eswa.2009.10.036","article-title":"Expert model for detection of epileptic activity in EEG signature","volume":"37","author":"Gandhi","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2019.07.006_bib0061","series-title":"Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI)","first-page":"1137","article-title":"A study of cross-validation and bootstrap for accuracy estimation and model selection","volume":"2","author":"Kohavi","year":"1995"},{"issue":"4","key":"10.1016\/j.neucom.2019.07.006_bib0062","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1109\/TMI.2006.871549","article-title":"Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)","volume":"25","author":"Suzuki","year":"2006","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2019.07.006_bib0063","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.neucom.2018.05.103","article-title":"Pixel-wise regression using U-Net and its application on pansharpening","volume":"312","author":"Yao","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2019.07.006_bib0064","series-title":"Proceedings of the 27th International Conference on Machine Learning (ICML)","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"Nair","year":"2010"},{"issue":"3","key":"10.1016\/j.neucom.2019.07.006_bib0065","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.neucom.2019.07.006_bib0066","series-title":"Proceedings of the COMPSTAT\u20192010","first-page":"177","article-title":"Large-scale machine learning with stochastic gradient descent","author":"Bottou","year":"2010"},{"key":"10.1016\/j.neucom.2019.07.006_bib0067","unstructured":"D.P. Kingma, J. Ba, Adam: a method for stochastic optimization, arXiv:1412.6980, (2014)."},{"key":"10.1016\/j.neucom.2019.07.006_bib0068","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"755","article-title":"Interactive graph cut based segmentation with shape priors","volume":"1","author":"Freedman","year":"2005"},{"issue":"1","key":"10.1016\/j.neucom.2019.07.006_bib0069","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s12880-015-0068-x","article-title":"Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool","volume":"15","author":"Taha","year":"2015","journal-title":"BMC Med. Imaging"},{"key":"10.1016\/j.neucom.2019.07.006_bib0070","series-title":"Proceedings of the Annual International Conference of the Engineering in Medicine and Biology Society","first-page":"7186","article-title":"Evaluation of segmentation algorithms for medical imaging","author":"Fenster","year":"2005"},{"key":"10.1016\/j.neucom.2019.07.006_bib0071","series-title":"Proceedings of the IEEE International Symposium on Signal Processing and its Applications (ISSPA)","first-page":"148","article-title":"A review of recent evaluation methods for image segmentation","volume":"1","author":"Zhang","year":"2001"},{"issue":"2","key":"10.1016\/j.neucom.2019.07.006_bib0072","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/S1076-6332(03)00671-8","article-title":"Statistical validation of image segmentation quality based on a spatial overlap index","volume":"11","author":"Zou","year":"2004","journal-title":"Acad. Radiol."},{"issue":"3","key":"10.1016\/j.neucom.2019.07.006_bib0073","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1145\/1041613.1041614","article-title":"Software unit profiles & kiviat figures","volume":"2","author":"Kolence","year":"1973","journal-title":"ACM SIGMETRICS Perform. Eval. Rev."},{"issue":"Jan","key":"10.1016\/j.neucom.2019.07.006_bib0074","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Dem\u0161ar","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.neucom.2019.07.006_sbref0072","series-title":"Proceedings of the Advances in Neural Information Processing Systems (NIPS)","first-page":"2672","article-title":"Generative adversarial nets","author":"Goodfellow","year":"2014"},{"key":"10.1016\/j.neucom.2019.07.006_bib0076","series-title":"Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI)","first-page":"734","article-title":"GAN-based synthetic brain MR image generation","author":"Han","year":"2018"},{"key":"10.1016\/j.neucom.2019.07.006_sbref0074","series-title":"Proceedings of the International Conference on Learning Representations (ICLR)","article-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2014"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231219309245?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231219309245?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2019,10,16]],"date-time":"2019-10-16T13:36:05Z","timestamp":1571232965000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231219309245"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11]]},"references-count":78,"alternative-id":["S0925231219309245"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2019.07.006","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2019,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2019.07.006","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}