Heckemann RA, Hammers A, Rueckert D, Aviv RI, Harvey CJ, Hajnal JV. Automated volumetry on MR mind pictures can assist diagnostic determination making. BMC Med Imaging. 2008;8(1):1–6.
Morin A, Samper-Gonzalez J, Bertrand A, Ströer S, Dormont D, Mendes A, et al. Accuracy of MRI Classification Algorithms in a Tertiary Reminiscence Heart Scientific Routine Cohort. J Alzheimer Dis. 2020;74(4):1157–66.
Lee JY, Oh SW, Chung MS, Park JE, Moon Y, Jeon HJ, et al. Clinically obtainable software program for automated mind volumetry: comparisons of quantity measurements and validation of intermethod reliability. Korean J Radiol. 2021;22(3):405.
Yu Q, Mai Y, Ruan Y, Luo Y, Zhao L, Fang W, et al. An MRI-based technique for differentiation of frontotemporal dementia and Alzheimer’s illness. Alzheimer Res Ther. 2021;13(1):1–12.
Zaki LA, Vernooij MW, Smits M, Tolman C, Papma JM, Visser JJ, et al. Evaluating two synthetic intelligence software program packages for normative mind volumetry in reminiscence clinic imaging. Neuroradiology. 2022;64:1–8.
Koikkalainen J, Rhodius-Meester H, Tolonen A, Barkhof F, Tijms B, Lemstra AW, et al. Differential analysis of neurodegenerative illnesses utilizing structural MRI knowledge. NeuroImage Clin. 2016;11:435–49.
Ma D, Lu D, Popuri Okay, Wang L, Beg MF, Initiative ADN. Differential analysis of frontotemporal dementia, alzheimer’s illness, and regular ageing utilizing a multi-scale multi-type function generative adversarial deep neural community on structural magnetic resonance pictures. Entrance Neurosci. 2020;14:853.
Penny WD, Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE. Statistical parametric mapping: the evaluation of useful mind pictures. Elsevier; 2011.
Avants BB, Tustison NJ, Stauffer M, Track G, Wu B, Gee JC. The Perception ToolKit picture registration framework. Entrance Neuroinformatics. 2014;8:44.
Mark J, Christian FB, Timothy EB, Mark WW, Stephen MS. FSL. NeuroImage. 2012;62(2):782–90.
Ashburner J, Friston KJ. Unified segmentation. NeuroImage. 2005;26(3):839–51.
Zhang Y, Brady M, Smith S. Segmentation of mind MR pictures by means of a hidden Markov random area mannequin and the expectation-maximization algorithm. IEEE Trans Med Imaging. 2001;20(1):45–57.
Avants BB, Tustison NJ, Wu J, Prepare dinner PA, Gee JC. An open supply multivariate framework for n-tissue segmentation with analysis on public knowledge. Neuroinformatics. 2011;9(4):381–400.
Wang H, Yushkevich P. Multi-atlas segmentation with joint label fusion and corrective learning-an open supply implementation. Entrance Neuroinformatics. 2013;7:27.
Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical picture segmentation. In: Worldwide Convention on Medical picture computing and computer-assisted intervention. Springer; 2015. pp. 234–41.
Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Internet: studying dense volumetric segmentation from sparse annotation. In: Worldwide convention on medical picture computing and computer-assisted intervention. Springer; 2016. pp. 424–32.
Han X. MR-Primarily based Artificial CT Technology Utilizing a Deep Convolutional Neural Community Methodology. Med Phys. 2017;44(4):1408–19.
Shiri I, Ghafarian P, Geramifar P, Leung KHY, Ghelichoghli M, Oveisi M, et al. Direct attenuation correction of mind PET pictures utilizing solely emission knowledge through a deep convolutional encoder-decoder (Deep-DAC). Eur Radiol. 2019;29(12):6867–79.
Gong Okay, Yang J, Kim Okay, El Fakhri G, Web optimization Y, Li Q. Attenuation correction for mind PET imaging utilizing deep neural community based mostly on Dixon and ZTE MR pictures. Phys Med Biol. 2018;63(12):125011.
Ladefoged CN, Marner L, Hindsholm A, Legislation I, Højgaard L, Andersen FL. Deep studying based mostly attenuation correction of PET/MRI in pediatric mind tumor sufferers: analysis in a medical setting. Entrance Neurosci. 2019;12:1005.
Spuhler KD, Gardus J, Gao Y, DeLorenzo C, Parsey R, Huang C. Synthesis of patient-specific transmission knowledge for PET attenuation correction for PET/MRI neuroimaging utilizing a convolutional neural community. J Nuclear Med. 2019;60(4):555–60.
Yang J, Park D, Gullberg GT, Web optimization Y. Joint correction of attenuation and scatter in picture area utilizing deep convolutional neural networks for devoted mind 18F-FDG PET. Phys Med Biol. 2019;64(7):075019.
Neppl S, Landry G, Kurz C, Hansen DC, Hoyle B, Stöcklein S, et al. Analysis of proton and photon dose distributions recalculated on 2D and 3D Unet-generated pseudoCTs from T1-weighted MR head scans. Acta Oncol. 2019;58(10):1429–34.
Wolterink JM, Dinkla AM, Savenije MH, Seevinck PR, van den Berg CA, Išgum I. Deep MR to CT synthesis utilizing unpaired knowledge. In: Worldwide workshop on simulation and synthesis in medical imaging. Springer; 2017. pp. 14–23.
Chen Y, Shi F, Christodoulou AG, Xie Y, Zhou Z, Li D. Environment friendly and correct MRI super-resolution utilizing a generative adversarial community and 3D multi-level densely related community. In: Worldwide Convention on Medical Picture Computing and Laptop-Assisted Intervention. Springer; 2018. pp. 91–9.
Gu J, Li Z, Wang Y, Yang H, Qiao Z, Yu J. Deep generative adversarial networks for thin-section toddler MR picture reconstruction. IEEE Entry. 2019;7:68290–304.
Kim KH, Do WJ, Park SH. Bettering decision of MR pictures with an adversarial community incorporating pictures with totally different distinction. Med Phys. 2018;45(7):3120–31.
Dinkla AM, Wolterink JM, Maspero M, Savenije MH, Verhoeff JJ, Seravalli E, et al. MR-only mind radiation remedy: dosimetric analysis of artificial CTs generated by a dilated convolutional neural community. Int J Radiat Oncol* Biol* Phys. 2018;102(4):801–12.
Emami H, Dong M, Nejad-Davarani SP, Glide-Hurst CK. Producing artificial CTs from magnetic resonance pictures utilizing generative adversarial networks. Med Phys. 2018;45(8):3627–36.
Nie D, Trullo R, Lian J, Wang L, Petitjean C, Ruan S, et al. Medical picture synthesis with deep convolutional adversarial networks. IEEE Trans Biomed Eng. 2018;65(12):2720–30.
Dar SU, Yurt M, Karacan L, Erdem A, Erdem E, Çukur T. Picture synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans Med Imaging. 2019;38(10):2375–88.
Yu B, Zhou L, Wang L, Shi Y, Fripp J, Bourgeat P. Ea-GANs: edge-aware generative adversarial networks for cross-modality MR picture synthesis. IEEE Trans Med Imaging. 2019;38(7):1750–62.
Li H, Paetzold JC, Sekuboyina A, Kofler F, Zhang J, Kirschke JS, et al. DiamondGAN: unified multi-modal generative adversarial networks for MRI sequences synthesis. In: Worldwide Convention on Medical Picture Computing and Laptop-Assisted Intervention. Springer; 2019. pp. 795–803.
Sharma A, Hamarneh G. Lacking MRI pulse sequence synthesis utilizing multi-modal generative adversarial community. IEEE Trans Med Imaging. 2019;39(4):1170–83.
Benou A, Veksler R, Friedman A, Raviv TR. Ensemble of knowledgeable deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences. Med Picture Anal. 2017;42:145–59.
Jiang D, Dou W, Vosters L, Xu X, Solar Y, Tan T. Denoising of 3D magnetic resonance pictures with multi-channel residual studying of convolutional neural community. Jpn J Radiol. 2018;36(9):566–74.
Ran M, Hu J, Chen Y, Chen H, Solar H, Zhou J, et al. Denoising of 3D magnetic resonance pictures utilizing a residual encoder-decoder Wasserstein generative adversarial community. Med Picture Anal. 2019;55:165–80.
Hashimoto F, Ohba H, Ote Okay, Teramoto A, Tsukada H. Dynamic PET picture denoising utilizing deep convolutional neural networks with out prior coaching datasets. IEEE Entry. 2019;7:96594–603.
Du J, Wang L, Liu Y, Zhou Z, He Z, Jia Y. Mind mri super-resolution utilizing 3d dilated convolutional encoder-decoder community. IEEE Entry. 2020;8:18938–50.
Pham CH, Ducournau A, Fablet R, Rousseau F. Mind MRI super-resolution utilizing deep 3D convolutional networks. In: 2017 IEEE ISBI. IEEE; 2017. pp. 197–200.
Zeng Okay, Zheng H, Cai C, Yang Y, Zhang Okay, Chen Z. Simultaneous single-and multi-contrast super-resolution for mind MRI pictures based mostly on a convolutional neural community. Comput Biol Med. 2018;99:133–41.
Dewey BE, Zhao C, Reinhold JC, Carass A, Fitzgerald KC, Sotirchos ES, et al. DeepHarmony: a deep studying method to distinction harmonization throughout scanner modifications. Magn Reson Imaging. 2019;64:160–70.
Xu C, Zhang D, Chong J, Chen B, Li S. Synthesis of gadolinium-enhanced liver tumors on nonenhanced liver MR pictures utilizing pixel-level graph reinforcement studying. Med Picture Anal. 2021;69:101976.
Web optimization M, Kim D, Lee Okay, Hong S, Bae JS, Kim JH, et al. Neural Distinction Enhancement of CT Picture. In: Proceedings of the IEEE/CVF Winter Convention on Functions of Laptop Imaginative and prescient. IEEE; 2021. pp. 3973–82.
Bône A, Ammari S, Lamarque JP, Elhaik M, Chouzenoux É, Nicolas F, et al. Distinction-enhanced mind MRI synthesis with deep studying: key enter modalities and asymptotic efficiency. In: 2021 IEEE ISBI. IEEE; 2021. pp. 1159–63.
Kleesiek J, Morshuis JN, Isensee F, Deike-Hofmann Okay, Paech D, Kickingereder P, et al. Can digital distinction enhancement in mind MRI change gadolinium?: a feasibility research. Investig Radiol. 2019;54(10):653–60.
Solar H, Liu X, Feng X, Liu C, Zhu N, Gjerswold-Selleck SJ, et al. Substituting Gadolinium in Mind MRI Utilizing DeepContrast. In: 2020 IEEE ISBI. IEEE; 2020. pp. 908–12.
Bottani S, Thibeau-Sutre E, Maire A, Ströer S, Dormont D, Colliot O, et al. Homogenization of mind MRI from a medical knowledge warehouse utilizing contrast-enhanced to non-contrast-enhanced picture translation with U-Internet derived fashions. In: SPIE Medical Imaging 2022. vol. 12032. SPIE; 2022. pp. 576–82.
Bottani S, Burgos N, Maire A, Wild A, Ströer S, Dormont D, et al. Automated high quality management of mind T1-weighted magnetic resonance pictures for a medical knowledge warehouse. Med Picture Anal. 2022;75:102219.
Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, et al. The mind imaging knowledge construction, a format for organizing and describing outputs of neuroimaging experiments. Sci Knowledge. 2016;3(1):1–9.
Routier A, Burgos N, Díaz M, Bacci M, Bottani S, El-Rifai O, et al. Clinica: An Open-Supply Software program Platform for Reproducible Scientific Neuroscience Research. Entrance Neuroinformatics. 2021;15:39. https://doi.org/10.3389/fninf.2021.689675.
Tustison NJ, Avants BB, Prepare dinner PA, Zheng Y, Egan A, Yushkevich PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010;29(6):1310–20.
Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic picture registration with cross-correlation: evaluating automated labeling of aged and neurodegenerative mind. Med Picture Anal. 2008;12(1):26–41.
Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-González J, Routier A, Bottani S, et al. Convolutional Neural Networks for Classification of Alzheimer’s Illness: Overview and Reproducible Analysis. Med Picture Anal. 2020;63:101694.
Milletari F, Navab N, Ahmadi SA. V-net: Totally convolutional neural networks for volumetric medical picture segmentation. In: 2016 fourth worldwide convention on 3D imaginative and prescient (3DV). IEEE; 2016. pp. 565–71.
Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa Okay, et al. Consideration U-Internet: Studying The place to Search for the Pancreas. In: Medical Imaging with Deep Studying – MIDL 2018. 2018.
Wang W, Chen C, Ding M, Yu H, Zha S, Li J. Transbts: Multimodal mind tumor segmentation utilizing transformer. In: Worldwide Convention on Medical Picture Computing and Laptop-Assisted Intervention. Springer; 2021. pp. 109–19.
He Okay, Zhang X, Ren S, Solar J. Id mappings in deep residual networks. In: European convention on laptop imaginative and prescient. Springer; 2016. pp. 630–45.
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. Adv Neural Inf Course of Syst. 2014;27.
Mirza M, Osindero S. Conditional generative adversarial nets. 2014. arXiv:14111784.
Isola P, Zhu JY, Zhou T, Efros AA. Picture-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE convention on laptop imaginative and prescient and sample recognition. IEEE; 2017. pp. 1125–34.
Wei W, Poirion E, Bodini B, Durrleman S, Ayache N, Stankoff B, et al. Predicting PET-derived demyelination from multimodal MRI utilizing sketcher-refiner adversarial coaching for a number of sclerosis. Med Picture Anal. 2019;58:101546.
Choi H, Lee DS. Technology of structural MR pictures from amyloid PET: software to MR-less quantification. J Nucl Med. 2018;59(7):1111–7.
Mao X, Li Q, Xie H, Lau RY, Wang Z, Paul Smolley S. Least squares generative adversarial networks. In: Proceedings of the IEEE worldwide convention on laptop imaginative and prescient. IEEE; 2017. pp. 2794–802.
Morid MA, Borjali A, Del Fiol G. A scoping assessment of switch studying analysis on medical picture evaluation utilizing ImageNet. Comput Biol Med. 2021;128:104115.
Mustafa B, Loh A, Freyberg J, MacWilliams P, Wilson M, McKinney SM, et al. Supervised switch studying at scale for medical imaging. 2021. arXiv preprint arXiv:210105913.
Salman H, Ilyas A, Engstrom L, Kapoor A, Madry A. Do adversarially strong imagenet fashions switch higher? Adv Neural Inf Course of Syst. 2020;33:3533–45.
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Picture high quality evaluation: from error visibility to structural similarity. IEEE Trans Picture Course of. 2004;13(4):600–12.
Isensee F, Schell M, Pflueger I, Brugnara G, Bonekamp D, Neuberger U, et al. Automated mind extraction of multisequence MRI utilizing synthetic neural networks. Hum Mind Mapp. 2019;40(17):4952–64.
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, et al. Reproducible analysis of classification strategies in Alzheimer’s illness: Framework and software to MRI and PET knowledge. NeuroImage. 2018;183:504–21.
Yi X, Walia E, Babyn P. Generative adversarial community in medical imaging: A assessment. Med Picture Anal. 2019;58:101552.
Burgos N, Bottani S, Faouzi J, Thibeau-Sutre E, Colliot O. Deep studying for mind issues: from knowledge processing to illness remedy. Transient Bioinforma. 2021;22(2):1560–76.
Daniel C, Salamanca E. Hospital Databases. In: Nordlinger B, Villani C, Rus D, editors. Healthcare and Synthetic Intelligence. Springer; 2020. p. 57–67.
Zhao H, Gallo O, Frosio I, Kautz J. Loss capabilities for picture restoration with neural networks. IEEE Trans Comput Imaging. 2016;3(1):47–57.
Zhu JY, Park T, Isola P, Efros AA. Unpaired image-to-image translation utilizing cycle-consistent adversarial networks. In: Proceedings of the IEEE worldwide convention on laptop imaginative and prescient. IEEE; 2017. pp. 2223–32.
Cackowski S, Barbier EL, Dojat M, Christen T. ImUnity: a generalizable VAE-GAN answer for multicenter MR picture harmonization. Med Picture Anal. 2023;88:102799.
Bottani S. Machine studying for neuroimaging utilizing a really massive scale medical datawarehouse [Ph.D. thesis]. Sorbonne Université; 2022.
