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For: Ou Y, Akbari H, Bilello M, Da X, Davatzikos C. Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights. IEEE Trans Med Imaging 2014;33:2039-65. [PMID: 24951685 DOI: 10.1109/TMI.2014.2330355] [Cited by in Crossref: 83] [Cited by in F6Publishing: 43] [Article Influence: 10.4] [Reference Citation Analysis]
Number Citing Articles
1 Ou Y, Zöllei L, Da X, Retzepi K, Murphy SN, Gerstner ER, Rosen BR, Grant PE, Kalpathy-Cramer J, Gollub RL. Field of View Normalization in Multi-Site Brain MRI. Neuroinformatics 2018;16:431-44. [PMID: 29353341 DOI: 10.1007/s12021-018-9359-z] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
2 Sotardi S, Gollub RL, Bates SV, Weiss R, Murphy SN, Grant PE, Ou Y. Voxelwise and Regional Brain Apparent Diffusion Coefficient Changes on MRI from Birth to 6 Years of Age. Radiology 2021;298:415-24. [PMID: 33289612 DOI: 10.1148/radiol.2020202279] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
3 Qin B, Shen Z, Fu Z, Zhou Z, Lv Y, Bao J. Joint-Saliency Structure Adaptive Kernel Regression with Adaptive-Scale Kernels for Deformable Registration of Challenging Images. IEEE Access 2018;6:330-43. [DOI: 10.1109/access.2017.2762901] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
4 Young R, Maga AM. Performance of single and multi-atlas based automated landmarking methods compared to expert annotations in volumetric microCT datasets of mouse mandibles. Front Zool 2015;12:33. [PMID: 26628903 DOI: 10.1186/s12983-015-0127-8] [Cited by in Crossref: 12] [Cited by in F6Publishing: 7] [Article Influence: 1.7] [Reference Citation Analysis]
5 Weiss Lucas C, Faymonville AM, Loução R, Schroeter C, Nettekoven C, Oros-Peusquens AM, Langen KJ, Shah NJ, Stoffels G, Neuschmelting V, Blau T, Neuschmelting H, Hellmich M, Kocher M, Grefkes C, Goldbrunner R. Surgery of Motor Eloquent Glioblastoma Guided by TMS-Informed Tractography: Driving Resection Completeness Towards Prolonged Survival. Front Oncol 2022;12:874631. [PMID: 35692752 DOI: 10.3389/fonc.2022.874631] [Reference Citation Analysis]
6 Machado I, Toews M, George E, Unadkat P, Essayed W, Luo J, Teodoro P, Carvalho H, Martins J, Golland P, Pieper S, Frisken S, Golby A, Wells Iii W, Ou Y. Deformable MRI-Ultrasound registration using correlation-based attribute matching for brain shift correction: Accuracy and generality in multi-site data. Neuroimage 2019;202:116094. [PMID: 31446127 DOI: 10.1016/j.neuroimage.2019.116094] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.7] [Reference Citation Analysis]
7 Yang X, Han X, Park E, Aylward S, Kwitt R, Niethammer M. Registration of Pathological Images. Simul Synth Med Imaging 2016;9968:97-107. [PMID: 29896582 DOI: 10.1007/978-3-319-46630-9_10] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 1.3] [Reference Citation Analysis]
8 Weiss RJ, Bates SV, Song Y, Zhang Y, Herzberg EM, Chen YC, Gong M, Chien I, Zhang L, Murphy SN, Gollub RL, Grant PE, Ou Y. Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy. J Transl Med 2019;17:385. [PMID: 31752923 DOI: 10.1186/s12967-019-2119-5] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
9 Morton SU, Vyas R, Gagoski B, Vu C, Litt J, Larsen RJ, Kuchan MJ, Lasekan JB, Sutton BP, Grant PE, Ou Y. Maternal Dietary Intake of Omega-3 Fatty Acids Correlates Positively with Regional Brain Volumes in 1-Month-Old Term Infants. Cereb Cortex 2020;30:2057-69. [PMID: 31711132 DOI: 10.1093/cercor/bhz222] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
10 Koutsouleris N, Meisenzahl EM, Borgwardt S, Riecher-Rössler A, Frodl T, Kambeitz J, Köhler Y, Falkai P, Möller HJ, Reiser M, Davatzikos C. Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. Brain 2015;138:2059-73. [PMID: 25935725 DOI: 10.1093/brain/awv111] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
11 Willette AA, Modanlo N, Kapogiannis D; Alzheimer’s Disease Neuroimaging Initiative. Insulin resistance predicts medial temporal hypermetabolism in mild cognitive impairment conversion to Alzheimer disease. Diabetes 2015;64:1933-40. [PMID: 25576061 DOI: 10.2337/db14-1507] [Cited by in Crossref: 63] [Cited by in F6Publishing: 61] [Article Influence: 9.0] [Reference Citation Analysis]
12 Borovec J, Kybic J, Arganda-Carreras I, Sorokin DV, Bueno G, Khvostikov AV, Bakas S, Chang EI, Heldmann S, Kartasalo K, Latonen L, Lotz J, Noga M, Pati S, Punithakumar K, Ruusuvuori P, Skalski A, Tahmasebi N, Valkonen M, Venet L, Wang Y, Weiss N, Wodzinski M, Xiang Y, Xu Y, Yan Y, Yushkevich P, Zhao S, Munoz-Barrutia A. ANHIR: Automatic Non-Rigid Histological Image Registration Challenge. IEEE Trans Med Imaging 2020;39:3042-52. [PMID: 32275587 DOI: 10.1109/TMI.2020.2986331] [Cited by in Crossref: 12] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
13 Andresen J, Kepp T, Ehrhardt J, Burchard CV, Roider J, Handels H. Deep learning-based simultaneous registration and unsupervised non-correspondence segmentation of medical images with pathologies. Int J Comput Assist Radiol Surg 2022. [PMID: 35239133 DOI: 10.1007/s11548-022-02577-4] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Zimmerman BE, Johnson S, Odeen H, Shea J, Foote MD, Winkler N, Joshi SC, Payne A. Learning Multiparametric Biomarkers for Assessing MR-Guided Focused Ultrasound Treatment of Malignant Tumors. IEEE Trans Biomed Eng 2021;68:1737-47. [PMID: 32946378 DOI: 10.1109/TBME.2020.3024826] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
15 Rozycki M, Satterthwaite TD, Koutsouleris N, Erus G, Doshi J, Wolf DH, Fan Y, Gur RE, Gur RC, Meisenzahl EM, Zhuo C, Yin H, Yan H, Yue W, Zhang D, Davatzikos C. Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals. Schizophr Bull 2018;44:1035-44. [PMID: 29186619 DOI: 10.1093/schbul/sbx137] [Cited by in Crossref: 60] [Cited by in F6Publishing: 56] [Article Influence: 20.0] [Reference Citation Analysis]
16 Vedmurthy P, Pinto ALR, Lin DDM, Comi AM, Ou Y; BCH-KKI SWS Pre-symptomatic Biomarker Working Group. Study protocol: retrospectively mining multisite clinical data to presymptomatically predict seizure onset for individual patients with Sturge-Weber. BMJ Open 2022;12:e053103. [PMID: 35121603 DOI: 10.1136/bmjopen-2021-053103] [Reference Citation Analysis]
17 Tang Z, Fan Y. Groupwise Image Registration Guided by a Dynamic Digraph of Images. Neuroinformatics 2016;14:131-45. [PMID: 26585712 DOI: 10.1007/s12021-015-9285-2] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 0.8] [Reference Citation Analysis]
18 Doshi J, Erus G, Ou Y, Resnick SM, Gur RC, Gur RE, Satterthwaite TD, Furth S, Davatzikos C; Alzheimer's Neuroimaging Initiative. MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection. Neuroimage 2016;127:186-95. [PMID: 26679328 DOI: 10.1016/j.neuroimage.2015.11.073] [Cited by in Crossref: 95] [Cited by in F6Publishing: 84] [Article Influence: 13.6] [Reference Citation Analysis]
19 Pappas I, Hector H, Haws K, Curran B, Kayser AS, D'Esposito M. Improved normalization of lesioned brains via cohort-specific templates. Hum Brain Mapp 2021;42:4187-204. [PMID: 34143540 DOI: 10.1002/hbm.25474] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
20 Koutsouleris N, Meisenzahl EM, Borgwardt S, Riecher-Rössler A, Frodl T, Kambeitz J, Köhler Y, Falkai P, Möller HJ, Reiser M, Davatzikos C. Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. Brain 2015;138:2059-73. [PMID: 25935725 DOI: 10.1093/brain/awv111] [Cited by in Crossref: 90] [Cited by in F6Publishing: 76] [Article Influence: 12.9] [Reference Citation Analysis]
21 He S, Pereira D, David Perez J, Gollub RL, Murphy SN, Prabhu S, Pienaar R, Robertson RL, Ellen Grant P, Ou Y. Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan. Med Image Anal 2021;72:102091. [PMID: 34038818 DOI: 10.1016/j.media.2021.102091] [Reference Citation Analysis]
22 Zöllei L, Iglesias JE, Ou Y, Grant PE, Fischl B. Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years. Neuroimage 2020;218:116946. [PMID: 32442637 DOI: 10.1016/j.neuroimage.2020.116946] [Cited by in Crossref: 19] [Cited by in F6Publishing: 11] [Article Influence: 9.5] [Reference Citation Analysis]
23 Chou MC, Ko CH, Hsieh TJ, Chang JM, Chung WS. A preliminary report of longitudinal white matter alterations in patients with end-stage renal disease: A three-year diffusion tensor imaging study. PLoS One 2019;14:e0215942. [PMID: 31039171 DOI: 10.1371/journal.pone.0215942] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Ou Y, Zöllei L, Retzepi K, Castro V, Bates SV, Pieper S, Andriole KP, Murphy SN, Gollub RL, Grant PE. Using clinically acquired MRI to construct age-specific ADC atlases: Quantifying spatiotemporal ADC changes from birth to 6-year old. Hum Brain Mapp 2017;38:3052-68. [PMID: 28371107 DOI: 10.1002/hbm.23573] [Cited by in Crossref: 18] [Cited by in F6Publishing: 16] [Article Influence: 3.6] [Reference Citation Analysis]
25 Rah JC, Feng L, Druckmann S, Lee H, Kim J. From a meso- to micro-scale connectome: array tomography and mGRASP. Front Neuroanat 2015;9:78. [PMID: 26089781 DOI: 10.3389/fnana.2015.00078] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 1.3] [Reference Citation Analysis]
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27 Pisapia JM, Akbari H, Rozycki M, Thawani JP, Storm PB, Avery RA, Vossough A, Fisher MJ, Heuer GG, Davatzikos C. Predicting pediatric optic pathway glioma progression using advanced magnetic resonance image analysis and machine learning. Neurooncol Adv 2020;2:vdaa090. [PMID: 32885166 DOI: 10.1093/noajnl/vdaa090] [Reference Citation Analysis]
28 Visser M, Petr J, Müller DMJ, Eijgelaar RS, Hendriks EJ, Witte M, Barkhof F, van Herk M, Mutsaerts HJMM, Vrenken H, de Munck JC, De Witt Hamer PC. Accurate MR Image Registration to Anatomical Reference Space for Diffuse Glioma. Front Neurosci 2020;14:585. [PMID: 32581699 DOI: 10.3389/fnins.2020.00585] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
29 Williams B, Roesch E, Christakou A. Systematic validation of an automated thalamic parcellation technique using anatomical data at 3T. NeuroImage 2022. [DOI: 10.1016/j.neuroimage.2022.119340] [Reference Citation Analysis]
30 Vogel D, Shah A, Coste J, Lemaire JJ, Wårdell K, Hemm S. Anatomical brain structures normalization for deep brain stimulation in movement disorders. Neuroimage Clin 2020;27:102271. [PMID: 32446242 DOI: 10.1016/j.nicl.2020.102271] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
31 Pinto ALR, Ou Y, Sahin M, Grant PE. Quantitative Apparent Diffusion Coefficient Mapping May Predict Seizure Onset in Children With Sturge-Weber Syndrome. Pediatr Neurol 2018;84:32-8. [PMID: 29753575 DOI: 10.1016/j.pediatrneurol.2018.04.004] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 1.8] [Reference Citation Analysis]
32 Akbari H, Rathore S, Bakas S, Nasrallah MP, Shukla G, Mamourian E, Rozycki M, Bagley SJ, Rudie JD, Flanders AE, Dicker AP, Desai AS, O'Rourke DM, Brem S, Lustig R, Mohan S, Wolf RL, Bilello M, Martinez-Lage M, Davatzikos C. Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma. Cancer 2020;126:2625-36. [PMID: 32129893 DOI: 10.1002/cncr.32790] [Cited by in Crossref: 16] [Cited by in F6Publishing: 13] [Article Influence: 8.0] [Reference Citation Analysis]
33 Ou Y, Weinstein SP, Conant EF, Englander S, Da X, Gaonkar B, Hsieh MK, Rosen M, DeMichele A, Davatzikos C, Kontos D. Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy. Magn Reson Med 2015;73:2343-56. [PMID: 25046843 DOI: 10.1002/mrm.25368] [Cited by in Crossref: 15] [Cited by in F6Publishing: 17] [Article Influence: 1.9] [Reference Citation Analysis]
34 Cao X, Yang J, Zhang J, Wang Q, Yap PT, Shen D. Deformable Image Registration Using a Cue-Aware Deep Regression Network. IEEE Trans Biomed Eng 2018;65:1900-11. [PMID: 29993391 DOI: 10.1109/TBME.2018.2822826] [Cited by in Crossref: 38] [Cited by in F6Publishing: 14] [Article Influence: 9.5] [Reference Citation Analysis]
35 Atehortúa A, Garreau M, Simon A, Donal E, Lederlin M, Romero E. Fusion of 3D real-time echocardiography and cine MRI using a saliency analysis. Int J Comput Assist Radiol Surg 2020;15:277-85. [PMID: 31713090 DOI: 10.1007/s11548-019-02087-w] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
36 Hoque MZ, Keskinarkaus A, Nyberg P, Mattila T, Seppänen T. Whole slide image registration via multi-stained feature matching. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105301] [Reference Citation Analysis]
37 Mang A, Ruthotto L. A LAGRANGIAN GAUSS-NEWTON-KRYLOV SOLVER FOR MASS- AND INTENSITY-PRESERVING DIFFEOMORPHIC IMAGE REGISTRATION. SIAM J Sci Comput 2017;39:B860-85. [PMID: 29097881 DOI: 10.1137/17M1114132] [Cited by in Crossref: 16] [Cited by in F6Publishing: 2] [Article Influence: 3.2] [Reference Citation Analysis]
38 Chen T, Yuan M, Tang J, Lu L. Digital Analysis of Smart Registration Methods for Magnetic Resonance Images in Public Healthcare. Front Public Health 2022;10:896967. [DOI: 10.3389/fpubh.2022.896967] [Reference Citation Analysis]
39 Hernandez M. A Comparative Study of Different Variants of Newton--Krylov PDE-Constrained Stokes-LDDMM Parameterized in the Space of Band-Limited Vector Fields. SIAM J Imaging Sci 2019;12:1038-70. [DOI: 10.1137/18m1195310] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
40 Dubost F, Bruijne M, Nardin M, Dalca AV, Donahue KL, Giese AK, Etherton MR, Wu O, Groot M, Niessen W, Vernooij M, Rost NS, Schirmer MD. Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation. Med Image Anal 2020;63:101698. [PMID: 32339896 DOI: 10.1016/j.media.2020.101698] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
41 Gur RE, Gur RC. Sex differences in brain and behavior in adolescence: Findings from the Philadelphia Neurodevelopmental Cohort. Neurosci Biobehav Rev. 2016;70:159-170. [PMID: 27498084 DOI: 10.1016/j.neubiorev.2016.07.035] [Cited by in Crossref: 62] [Cited by in F6Publishing: 57] [Article Influence: 10.3] [Reference Citation Analysis]
42 Kim J, Duchin Y, Shamir RR, Patriat R, Vitek J, Harel N, Sapiro G. Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation. Hum Brain Mapp 2019;40:679-98. [PMID: 30379376 DOI: 10.1002/hbm.24404] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 3.5] [Reference Citation Analysis]
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44 Wårdell K, Nordin T, Vogel D, Zsigmond P, Westin C, Hariz M, Hemm S. Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization. Front Neurosci 2022;16:834026. [DOI: 10.3389/fnins.2022.834026] [Reference Citation Analysis]
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46 Nowinski WL. Towards an Architecture of a Multi-purpose, User-Extendable Reference Human Brain Atlas. Neuroinformatics 2021. [PMID: 34825350 DOI: 10.1007/s12021-021-09555-2] [Reference Citation Analysis]
47 Li X. Subject-Specific Head Model Generation by Mesh Morphing: A Personalization Framework and Its Applications. Front Bioeng Biotechnol 2021;9:706566. [PMID: 34733827 DOI: 10.3389/fbioe.2021.706566] [Reference Citation Analysis]
48 Hoopes A, Mora JS, Dalca AV, Fischl B, Hoffmann M. SynthStrip: skull-stripping for any brain image. Neuroimage 2022;260:119474. [PMID: 35842095 DOI: 10.1016/j.neuroimage.2022.119474] [Reference Citation Analysis]
49 Li H, Yan G, Luo W, Liu T, Wang Y, Liu R, Zheng W, Zhang Y, Li K, Zhao L, Limperopoulos C, Zou Y, Wu D. Mapping fetal brain development based on automated segmentation and 4D brain atlasing. Brain Struct Funct 2021;226:1961-72. [PMID: 34050792 DOI: 10.1007/s00429-021-02303-x] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
50 Chou MC, Lin JH, Wu MT. Gray and White Matter Changes Associated with Psychophysical Functions Induced by Diabolo Training in Young Men. Tomography 2022;8:858-68. [PMID: 35314647 DOI: 10.3390/tomography8020070] [Reference Citation Analysis]
51 Ou Y, Gollub RL, Retzepi K, Reynolds N, Pienaar R, Pieper S, Murphy SN, Grant PE, Zöllei L. Brain extraction in pediatric ADC maps, toward characterizing neuro-development in multi-platform and multi-institution clinical images. Neuroimage 2015;122:246-61. [PMID: 26260429 DOI: 10.1016/j.neuroimage.2015.08.002] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 1.4] [Reference Citation Analysis]
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53 Xiao Y, Rivaz H, Chabanas M, Fortin M, Machado I, Ou Y, Heinrich MP, Schnabel JA, Zhong X, Maier A, Wein W, Shams R, Kadoury S, Drobny D, Modat M, Reinertsen I. Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge. IEEE Trans Med Imaging 2020;39:777-86. [PMID: 31425023 DOI: 10.1109/TMI.2019.2935060] [Cited by in Crossref: 14] [Cited by in F6Publishing: 4] [Article Influence: 4.7] [Reference Citation Analysis]
54 Hernandez M. Efficient momentum conservation constrained PDE-LDDMM with Gauss–Newton–Krylov optimization, Semi-Lagrangian Runge–Kutta solvers, and the band-limited parameterization. Journal of Computational Science 2021;55:101470. [DOI: 10.1016/j.jocs.2021.101470] [Reference Citation Analysis]
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56 Anderson R, Maga AM. A Novel Procedure for Rapid Imaging of Adult Mouse Brains with MicroCT Using Iodine-Based Contrast. PLoS One 2015;10:e0142974. [PMID: 26571123 DOI: 10.1371/journal.pone.0142974] [Cited by in Crossref: 17] [Cited by in F6Publishing: 15] [Article Influence: 2.4] [Reference Citation Analysis]
57 Farnia P, Makkiabadi B, Alimohamadi M, Najafzadeh E, Basij M, Yan Y, Mehrmohammadi M, Ahmadian A. Photoacoustic-MR Image Registration Based on a Co-Sparse Analysis Model to Compensate for Brain Shift. Sensors (Basel) 2022;22:2399. [PMID: 35336570 DOI: 10.3390/s22062399] [Reference Citation Analysis]
58 Kalpathy-Cramer J, Chandra V, Da X, Ou Y, Emblem KE, Muzikansky A, Cai X, Douw L, Evans JG, Dietrich J, Chi AS, Wen PY, Stufflebeam S, Rosen B, Duda DG, Jain RK, Batchelor TT, Gerstner ER. Phase II study of tivozanib, an oral VEGFR inhibitor, in patients with recurrent glioblastoma. J Neurooncol. 2017;131:603-610. [PMID: 27853960 DOI: 10.1007/s11060-016-2332-5] [Cited by in Crossref: 34] [Cited by in F6Publishing: 33] [Article Influence: 5.7] [Reference Citation Analysis]
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60 Krüger J, Schultz S, Handels H, Ehrhardt J. Registration with probabilistic correspondences — Accurate and robust registration for pathological and inhomogeneous medical data. Computer Vision and Image Understanding 2020;190:102839. [DOI: 10.1016/j.cviu.2019.102839] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
61 Zeng K, Erus G, Sotiras A, Shinohara RT, Davatzikos C. Abnormality Detection via Iterative Deformable Registration and Basis-Pursuit Decomposition. IEEE Trans Med Imaging 2016;35:1937-51. [PMID: 27046847 DOI: 10.1109/TMI.2016.2538998] [Cited by in Crossref: 4] [Article Influence: 0.7] [Reference Citation Analysis]
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64 Lange FJ, Ashburner J, Smith SM, Andersson JLR. A Symmetric Prior for the Regularisation of Elastic Deformations: Improved anatomical plausibility in nonlinear image registration. Neuroimage 2020;219:116962. [PMID: 32497785 DOI: 10.1016/j.neuroimage.2020.116962] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]