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Biol.","volume":"58","author":"Schur","year":"2019"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0591-8"},{"issue":"3","key":"ref9","first-page":"730","article-title":"Molecular architecture of the SARS-CoV-2\n                        virus","volume-title":"Cell","volume":"183","author":"Yao","year":"2020"},{"issue":"1","key":"ref10","first-page":"2582","article-title":"Exploring an optimal wavelet-based filter for cryo-ET\n                        imaging","volume-title":"Sci. Rep.","volume":"8","author":"Huang","year":"2018"},{"key":"ref11","first-page":"61","article-title":"Noise models and cryo-EM drift correction with a\n                        direct-electron camera","volume-title":"Ultramicroscopy","volume":"131","author":"Shigematsu","year":"2013"},{"key":"ref12","article-title":"Noise2Noise: Learning image restoration without\n                        clean data","author":"Lehtinen","year":"2018"},{"issue":"1","key":"ref13","article-title":"Topaz-denoise: General deep denoising models for\n                        cryoEM and cryoET","volume-title":"Nature Commun.","volume":"11","author":"Bepler","year":"2020"},{"key":"ref14","first-page":"502","article-title":"Cryo-CARE: Content-aware image restoration for\n                        cryo-transmission electron microscopy data","volume-title":"Proc. IEEE 16th Int. Symp. Biomed.\n                        Imag.","author":"Buchholz"},{"key":"ref15","first-page":"2129","article-title":"Noise2void-learning denoising from single noisy\n                        images","volume-title":"Proc.\n                        IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","author":"Krull"},{"key":"ref16","doi-asserted-by":"crossref","DOI":"10.1101\/256792","article-title":"Generative adversarial networks as a tool to recover\n                        structural information from cryo-electron microscopy\n                    data","author":"Su","year":"2018"},{"issue":"7","key":"ref17","first-page":"3523","article-title":"Image segmentation using deep learning: A\n                        survey","volume-title":"IEEE Trans.\n                        Pattern Anal. Mach. Intell.","volume":"44","author":"Minaee","year":"2022"},{"key":"ref18","article-title":"Deep learning for cardiac image segmentation: A\n                        review","volume-title":"Front. Cardiovasc. Med.","volume":"7","author":"Chen","year":"2020"},{"key":"ref19","article-title":"A review on deep learning techniques applied to\n                        semantic segmentation","author":"Garcia-Garcia","year":"2017"},{"key":"ref20","first-page":"302","article-title":"A brief survey on semantic segmentation with deep\n                        learning","volume-title":"Neurocomputing","volume":"406","author":"Hao","year":"2020"},{"key":"ref21","first-page":"424","article-title":"3D U-net: Learning dense volumetric segmentation from\n                        sparse annotation","volume-title":"Proc. Int. Conf. Med.\n                        Image Comput. Comput.-Assisted Intervention","author":"\u00c7i\u00e7ek"},{"issue":"2","key":"ref22","first-page":"576","article-title":"AnatomyNet: Deep learning for fast and fully\n                        automated whole-volume segmentation of head and neck\n                    anatomy","volume-title":"Med. Phys.","volume":"46","author":"Zhu","year":"2019"},{"key":"ref23","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image\n                        segmentation","volume-title":"Proc. Int. Conf. Med. Image\n                        Comput. Comput.-Assisted Intervention","author":"Ronneberger"},{"key":"ref24","article-title":"Superhuman accuracy on the SNEMI3D connectomics\n                        challenge","author":"Lee","year":"2017"},{"key":"ref25","article-title":"3D densely convolutional networks for volumetric\n                        segmentation","author":"Bui","year":"2017"},{"key":"ref26","first-page":"378","article-title":"A skip-connected 3D DenseNet networks with\n                        adversarial training for volumetric segmentation","volume-title":"Proc. Int. MICCAI Brainlesion Workshop","author":"Bui"},{"key":"ref27","article-title":"Skip-connected 3D DenseNet for volumetric infant\n                        brain MRI segmentation","volume-title":"Biomed. Signal\n                        Process. Control","volume":"54","author":"Bui","year":"2019"},{"key":"ref28","first-page":"287","article-title":"Automatic 3D cardiovascular MR segmentation with\n                        densely-connected volumetric convnets","volume-title":"Proc. Int. Conf. Med. Image Comput. Comput.-Assisted\n                        Intervention","author":"Yu"},{"key":"ref29","first-page":"82031","article-title":"U-net and its variants for medical image\n                        segmentation: A review of theory and applications","volume-title":"IEEE Access","volume":"9","author":"Siddique","year":"2021"},{"key":"ref30","article-title":"Attention U-net: Learning where to look for the\n                        pancreas","author":"Oktay","year":"2018"},{"key":"ref31","first-page":"3","article-title":"UNet: A nested U-Net architecture for medical image\n                        segmentation","volume-title":"Proc. Int. Workshop Deep\n                        Learn. Med. Image Anal. Multimodal Learn. Clin. Decis. Support","author":"Zhou"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/icassp40776.2020.9053405"},{"issue":"11","key":"ref33","first-page":"1386","article-title":"Deep learning improves macromolecule\n                        identification in 3D cellular cryo-electron\n                    tomograms","volume-title":"Nature Methods","volume":"18","author":"Moebel","year":"2021"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102038"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/tvcg.2018.2796085"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/pacificvis.2019.00041"},{"issue":"4","key":"ref37","first-page":"1636","article-title":"A generative model for volume\n                        rendering","volume-title":"IEEE Trans.\n                        Vis. Comput. Graphics","volume":"25","author":"Berger","year":"2019"},{"key":"ref38","first-page":"118","article-title":"A rule-based tool for assisting colormap\n                        selection","volume-title":"Proc. Vis.","author":"Bergman"},{"key":"ref39","first-page":"79","article-title":"Semi-automatic generation of transfer functions for\n                        direct volume rendering","volume-title":"Proc. IEEE Symp. Volume Vis.","author":"Kindlmann"},{"issue":"2","key":"ref40","first-page":"192","article-title":"Visibility histograms and visibility-driven transfer\n                        functions","volume-title":"IEEE Trans.\n                        Vis. Comput. Graphics","volume":"17","author":"Correa","year":"2011"},{"issue":"6","key":"ref41","first-page":"1301","article-title":"Spatial conditioning of transfer functions using\n                        local material distributions","volume-title":"IEEE Trans. Vis. Comput. Graphics","volume":"16","author":"Lindholm","year":"2010"},{"issue":"7","key":"ref42","first-page":"450","article-title":"Automatic transfer function design for medical\n                        visualization using visibility distributions and projective color\n                        mapping","volume-title":"Computerized Med. Imag.\n                        Graph.","volume":"37","author":"Cai","year":"2013"},{"issue":"3","key":"ref43","first-page":"669","article-title":"State of the art in transfer functions for direct\n                        volume rendering","volume-title":"Comput. Graph.\n                        Forum","volume":"35","author":"Ljung","year":"2016"},{"key":"ref44","doi-asserted-by":"crossref","DOI":"10.1145\/3154353.3154357","article-title":"Transfer function optimization based on a combined\n                        model of visibility and saliency","volume-title":"Proc.\n                        33rd Spring Conf. Comput. 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Graph.","volume":"12","author":"Lundstrom","year":"2006"},{"issue":"6","key":"ref48","first-page":"1648","article-title":"Uncertainty visualization in medical volume rendering\n                        using probabilistic animation","volume-title":"IEEE Trans. Vis. Comput. Graphics","volume":"13","author":"Lundstr\u00f6m","year":"2007"},{"issue":"5","key":"ref49","first-page":"6","article-title":"2010 IEEE visualization contest winner: Interactive\n                        planning for brain tumor resections","volume-title":"IEEE Comput. Graph. Appl.","volume":"31","author":"Diepenbrock","year":"2011"},{"key":"ref50","article-title":"Pseudo-Label: The simple and efficient\n                        semi-supervised learning method for deep neural\n                    networks","volume":"3","author":"Lee","year":"2013","journal-title":"Proc. Workshop Challenges\n                        Representation Learn."},{"key":"ref51","first-page":"10687","article-title":"Self-training with noisy student improves ImageNet\n                        classification","volume-title":"Proc.\n                        IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","author":"Xie"},{"key":"ref52","first-page":"1226","article-title":"Ilastik: Interactive machine learning for\n                        (bio)image analysis","volume-title":"Nature\n                    Methods","volume":"16","author":"Berg","year":"2019"},{"key":"ref53","first-page":"810","article-title":"Semi-supervised medical image segmentation via\n                        learning consistency under transformations","volume-title":"Proc. Int. Conf. Med. Image Comput. Assist. Intervention","author":"Bortsova"},{"key":"ref54","article-title":"Distilling the knowledge in a neural\n                        network","author":"Hinton","year":"2015"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00707"},{"key":"ref56","article-title":"PyTorch: An imperative style, high-performance\n                        deep learning library","volume-title":"Proc. 33rd Int.\n                        Conf. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref57","article-title":"Adam: A method for stochastic\n                        optimization","author":"Kingma","year":"2014"},{"key":"ref58","article-title":"Mixed precision\n                    training","author":"Micikevicius","year":"2017"},{"issue":"8","key":"ref59","first-page":"630","article-title":"Picture thresholding using an iterative\n                        selection method","volume-title":"IEEE\n                        Trans. Syst. Man Cybern.","volume":"SMC-8","author":"Ridler","year":"1978"},{"issue":"4","key":"ref60","first-page":"548","article-title":"Local ambient occlusion in direct volume\n                        rendering","volume-title":"IEEE Trans.\n                        Vis. Comput. Graphics","volume":"16","author":"Hernell","year":"2010"},{"key":"ref61","article-title":"Robust Monte Carlo methods for light transport\n                        simulation","author":"Veach","year":"1997"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2019"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/83.366472"},{"issue":"3","key":"ref64","first-page":"377","article-title":"Moment-preserving thresolding: A new\n                        approach","volume-title":"Comput. Vis. Graph. Image\n                        Process.","volume":"29","author":"Tsai","year":"1985"},{"issue":"13","key":"ref65","first-page":"1605","article-title":"UCSF chimera\u2014A visualization system for\n                        exploratory research and analysis","volume-title":"J.\n                        Comput. Chem.","volume":"25","author":"Pettersen","year":"2004"},{"issue":"2","key":"ref66","first-page":"102","article-title":"Automated tilt series alignment and tomographic\n                        reconstruction in IMOD","volume-title":"J. Struct.\n                        Biol.","volume":"197","author":"Mastronarde","year":"2017"},{"issue":"11","key":"ref67","first-page":"1218","article-title":"The architecture of inactivated SARS-CoV-2 with\n                        postfusion spikes revealed by Cryo-EM and Cryo-ET","volume-title":"Structure","volume":"28","author":"Liu","year":"2020"},{"issue":"1","key":"ref68","first-page":"41","article-title":"Image thresholding by minimizing the measures of\n                        fuzziness","volume-title":"Pattern Recognit.","volume":"28","author":"Huang","year":"1995"},{"issue":"8","key":"ref69","first-page":"771","article-title":"An iterative algorithm for minimum cross entropy\n                        thresholding","volume-title":"Pattern Recognit.\n                        Lett.","volume":"19","author":"Li","year":"1998"},{"issue":"3","key":"ref70","first-page":"273","article-title":"A new method for gray-level picture thresholding\n                        using the entropy of the histogram","volume-title":"Comput.\n                        Vis. Graph. Image Process.","volume":"29","author":"Kapur","year":"1985"},{"issue":"6","key":"ref71","first-page":"532","article-title":"An analysis of histogram-based thresholding\n                        algorithms","volume-title":"CVGIP: Graphical Models Image\n                        Process.","volume":"55","author":"Glasbey","year":"1993"},{"issue":"1","key":"ref72","first-page":"41","article-title":"Minimum error thresholding","volume-title":"Pattern Recognit.","volume":"19","author":"Kittler","year":"1986"},{"issue":"1","key":"ref73","first-page":"62","article-title":"A threshold selection method from gray-level\n                        histograms","volume-title":"IEEE Trans.\n                        Syst., Man, Cybern.","volume":"SMC-9","author":"Otsu","year":"1979"},{"issue":"2","key":"ref74","first-page":"259","article-title":"Operations useful for similarity-invariant pattern\n                        recognition","volume-title":"J. ACM","volume":"9","author":"Doyle","year":"1962"},{"issue":"5","key":"ref75","first-page":"414","article-title":"Utilization of information measure as a means of\n                        image thresholding","volume-title":"CVGIP: Graphical Models\n                        Image Process.","volume":"56","author":"Shanbhag","year":"1994"},{"issue":"7","key":"ref76","first-page":"741","article-title":"Automatic measurement of sister chromatid exchange\n                        frequency","volume-title":"J. Histochemistry\n                        Cytochemistry","volume":"25","author":"Zack","year":"1977"},{"issue":"1","key":"ref77","article-title":"Gray level image edge detection using a hybrid\n                        model of cellular learning automata and stochastic cellular\n                        automata","volume-title":"Open Access Library J.","volume":"2","author":"Vatani","year":"2015"},{"key":"ref78","volume-title":"Morphological Image Analysis: Principles and Applications","author":"Soille","year":"2013"},{"issue":"4","key":"ref79","first-page":"388","article-title":"Automatic boundary detection of the left ventricle\n                        from cineangiograms","volume-title":"Comput. Biomed.\n                        Res.","volume":"5","author":"Chow","year":"1972"},{"key":"ref80","volume-title":"An\n                        Introduction to Digital Image Processing","author":"Niblack","year":"1985"},{"key":"ref81","first-page":"218","article-title":"Adaptive local thresholding for detection of nuclei\n                        in diversity stained cytology images","volume-title":"Proc. Int. Conf. Commun. Signal Process.","author":"Phansalkar"},{"issue":"2","key":"ref82","first-page":"225","article-title":"Adaptive document image\n                    binarization","volume-title":"Pattern Recognit.","volume":"33","author":"Sauvola","year":"2000"}],"container-title":["IEEE Transactions on Visualization and Computer Graphics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/2945\/10237330\/09806341.pdf?arnumber=9806341","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T09:39:06Z","timestamp":1725961146000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9806341\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,1]]},"references-count":82,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tvcg.2022.3186146","relation":{},"ISSN":["1077-2626","1941-0506","2160-9306"],"issn-type":[{"value":"1077-2626","type":"print"},{"value":"1941-0506","type":"electronic"},{"value":"2160-9306","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,1]]}}}