147 related articles for article (PubMed ID: 31546048)
1. Neuroimage signature from salient keypoints is highly specific to individuals and shared by close relatives.
Chauvin L; Kumar K; Wachinger C; Vangel M; de Guise J; Desrosiers C; Wells W; Toews M;
Neuroimage; 2020 Jan; 204():116208. PubMed ID: 31546048
[TBL] [Abstract][Full Text] [Related]
2. Efficient Pairwise Neuroimage Analysis Using the Soft Jaccard Index and 3D Keypoint Sets.
Chauvin L; Kumar K; Desrosiers C; Wells W; Toews M
IEEE Trans Med Imaging; 2022 Apr; 41(4):836-845. PubMed ID: 34699353
[TBL] [Abstract][Full Text] [Related]
3. BIK-BUS: biologically motivated 3D keypoint based on bottom-up saliency.
Filipe S; Itti L; Alexandre LA
IEEE Trans Image Process; 2015 Jan; 24(1):163-75. PubMed ID: 25420258
[TBL] [Abstract][Full Text] [Related]
4. A spatio-temporal reference model of the aging brain.
Huizinga W; Poot DHJ; Vernooij MW; Roshchupkin GV; Bron EE; Ikram MA; Rueckert D; Niessen WJ; Klein S;
Neuroimage; 2018 Apr; 169():11-22. PubMed ID: 29203452
[TBL] [Abstract][Full Text] [Related]
5. QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy.
Guha Roy A; Conjeti S; Navab N; Wachinger C;
Neuroimage; 2019 Feb; 186():713-727. PubMed ID: 30502445
[TBL] [Abstract][Full Text] [Related]
6. Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data.
Zhou T; Thung KH; Liu M; Shi F; Zhang C; Shen D
Med Image Anal; 2020 Feb; 60():101630. PubMed ID: 31927474
[TBL] [Abstract][Full Text] [Related]
7. DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders.
Marinescu RV; Eshaghi A; Lorenzi M; Young AL; Oxtoby NP; Garbarino S; Crutch SJ; Alexander DC;
Neuroimage; 2019 May; 192():166-177. PubMed ID: 30844504
[TBL] [Abstract][Full Text] [Related]
8. Multi-modal brain fingerprinting: A manifold approximation based framework.
Kumar K; Toews M; Chauvin L; Colliot O; Desrosiers C
Neuroimage; 2018 Dec; 183():212-226. PubMed ID: 30099077
[TBL] [Abstract][Full Text] [Related]
9. Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan.
Pomponio R; Erus G; Habes M; Doshi J; Srinivasan D; Mamourian E; Bashyam V; Nasrallah IM; Satterthwaite TD; Fan Y; Launer LJ; Masters CL; Maruff P; Zhuo C; Völzke H; Johnson SC; Fripp J; Koutsouleris N; Wolf DH; Gur R; Gur R; Morris J; Albert MS; Grabe HJ; Resnick SM; Bryan RN; Wolk DA; Shinohara RT; Shou H; Davatzikos C
Neuroimage; 2020 Mar; 208():116450. PubMed ID: 31821869
[TBL] [Abstract][Full Text] [Related]
10. A comparison of various MRI feature types for characterizing whole brain anatomical differences using linear pattern recognition methods.
Monté-Rubio GC; Falcón C; Pomarol-Clotet E; Ashburner J
Neuroimage; 2018 Sep; 178():753-768. PubMed ID: 29864520
[TBL] [Abstract][Full Text] [Related]
11. Whole volume brain extraction for multi-centre, multi-disease FLAIR MRI datasets.
Khademi A; Reiche B; DiGregorio J; Arezza G; Moody AR
Magn Reson Imaging; 2020 Feb; 66():116-130. PubMed ID: 31472262
[TBL] [Abstract][Full Text] [Related]
12. MarkVCID cerebral small vessel consortium: II. Neuroimaging protocols.
Lu H; Kashani AH; Arfanakis K; Caprihan A; DeCarli C; Gold BT; Li Y; Maillard P; Satizabal CL; Stables L; Wang DJJ; Corriveau RA; Singh H; Smith EE; Fischl B; van der Kouwe A; Schwab K; Helmer KG; Greenberg SM;
Alzheimers Dement; 2021 Apr; 17(4):716-725. PubMed ID: 33480157
[TBL] [Abstract][Full Text] [Related]
13. Making use of longitudinal information in pattern recognition.
Aksman LM; Lythgoe DJ; Williams SC; Jokisch M; Mönninghoff C; Streffer J; Jöckel KH; Weimar C; Marquand AF
Hum Brain Mapp; 2016 Dec; 37(12):4385-4404. PubMed ID: 27451934
[TBL] [Abstract][Full Text] [Related]
14. Evaluation of Ultrafast Wave-CAIPI MPRAGE for Visual Grading and Automated Measurement of Brain Tissue Volume.
Longo MGF; Conklin J; Cauley SF; Setsompop K; Tian Q; Polak D; Polackal M; Splitthoff D; Liu W; González RG; Schaefer PW; Kirsch JE; Rapalino O; Huang SY
AJNR Am J Neuroradiol; 2020 Aug; 41(8):1388-1396. PubMed ID: 32732274
[TBL] [Abstract][Full Text] [Related]
15. Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan.
He S; Pereira D; David Perez J; Gollub RL; Murphy SN; Prabhu S; Pienaar R; Robertson RL; Ellen Grant P; Ou Y
Med Image Anal; 2021 Aug; 72():102091. PubMed ID: 34038818
[TBL] [Abstract][Full Text] [Related]
16. The Pediatric Cerebellum in Inherited Neurodegenerative Disorders: A Pattern-recognition Approach.
Blaser SI; Steinlin M; Al-Maawali A; Yoon G
Neuroimaging Clin N Am; 2016 Aug; 26(3):373-416. PubMed ID: 27423800
[TBL] [Abstract][Full Text] [Related]
17. Defining multivariate normative rules for healthy aging using neuroimaging and machine learning: an application to Alzheimer's disease.
Andrade de Oliveira A; Carthery-Goulart MT; Oliveira Júnior PP; Carrettiero DC; Sato JR
J Alzheimers Dis; 2015; 43(1):201-12. PubMed ID: 25079801
[TBL] [Abstract][Full Text] [Related]
18. Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates.
Pipitone J; Park MT; Winterburn J; Lett TA; Lerch JP; Pruessner JC; Lepage M; Voineskos AN; Chakravarty MM;
Neuroimage; 2014 Nov; 101():494-512. PubMed ID: 24784800
[TBL] [Abstract][Full Text] [Related]
19. HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework.
Varol E; Sotiras A; Davatzikos C;
Neuroimage; 2017 Jan; 145(Pt B):346-364. PubMed ID: 26923371
[TBL] [Abstract][Full Text] [Related]
20. Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Cortical and Subcortical Features from MRI T1 Brain Images Utilizing Four Different Types of Datasets.
Toshkhujaev S; Lee KH; Choi KY; Lee JJ; Kwon GR; Gupta Y; Lama RK
J Healthc Eng; 2020; 2020():3743171. PubMed ID: 32952988
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]