177 related articles for article (PubMed ID: 34325148)
1. Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images.
Sundaresan V; Zamboni G; Rothwell PM; Jenkinson M; Griffanti L
Med Image Anal; 2021 Oct; 73():102184. PubMed ID: 34325148
[TBL] [Abstract][Full Text] [Related]
2. Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI.
Moeskops P; de Bresser J; Kuijf HJ; Mendrik AM; Biessels GJ; Pluim JPW; Išgum I
Neuroimage Clin; 2018; 17():251-262. PubMed ID: 29159042
[TBL] [Abstract][Full Text] [Related]
3. An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment.
Liang L; Zhou P; Lu W; Guo X; Ye C; Lv H; Wang T; Ma T
Comput Med Imaging Graph; 2021 Apr; 89():101873. PubMed ID: 33610084
[TBL] [Abstract][Full Text] [Related]
4. Automatic segmentation of white matter hyperintensities in T2-FLAIR with AQUA: A comparative validation study against conventional methods.
Lee S; Rieu Z; Kim RE; Lee M; Yen K; Yong J; Kim D
Brain Res Bull; 2023 Dec; 205():110825. PubMed ID: 38000477
[TBL] [Abstract][Full Text] [Related]
5. BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.
Griffanti L; Zamboni G; Khan A; Li L; Bonifacio G; Sundaresan V; Schulz UG; Kuker W; Battaglini M; Rothwell PM; Jenkinson M
Neuroimage; 2016 Nov; 141():191-205. PubMed ID: 27402600
[TBL] [Abstract][Full Text] [Related]
6. End-to-end volumetric segmentation of white matter hyperintensities using deep learning.
Farkhani S; Demnitz N; Boraxbekk CJ; Lundell H; Siebner HR; Petersen ET; Madsen KH
Comput Methods Programs Biomed; 2024 Mar; 245():108008. PubMed ID: 38290291
[TBL] [Abstract][Full Text] [Related]
7. Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images.
Sundaresan V; Zamboni G; Dinsdale NK; Rothwell PM; Griffanti L; Jenkinson M
Med Image Anal; 2021 Dec; 74():102215. PubMed ID: 34454295
[TBL] [Abstract][Full Text] [Related]
8. Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding.
Sundaresan V; Zamboni G; Le Heron C; Rothwell PM; Husain M; Battaglini M; De Stefano N; Jenkinson M; Griffanti L
Neuroimage; 2019 Nov; 202():116056. PubMed ID: 31376518
[TBL] [Abstract][Full Text] [Related]
9. White matter hyperintensities segmentation using the ensemble U-Net with multi-scale highlighting foregrounds.
Park G; Hong J; Duffy BA; Lee JM; Kim H
Neuroimage; 2021 Aug; 237():118140. PubMed ID: 33957235
[TBL] [Abstract][Full Text] [Related]
10. Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs.
Hong J; Park BY; Lee MJ; Chung CS; Cha J; Park H
Comput Methods Programs Biomed; 2020 Jan; 183():105065. PubMed ID: 31522090
[TBL] [Abstract][Full Text] [Related]
11. Automatic segmentation of white matter hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects.
Tran P; Thoprakarn U; Gourieux E; Dos Santos CL; Cavedo E; Guizard N; Cotton F; Krolak-Salmon P; Delmaire C; Heidelberg D; Pyatigorskaya N; Ströer S; Dormont D; Martini JB; Chupin M;
Neuroimage Clin; 2022; 33():102940. PubMed ID: 35051744
[TBL] [Abstract][Full Text] [Related]
12. DEWS (DEep White matter hyperintensity Segmentation framework): A fully automated pipeline for detecting small deep white matter hyperintensities in migraineurs.
Park BY; Lee MJ; Lee SH; Cha J; Chung CS; Kim ST; Park H
Neuroimage Clin; 2018; 18():638-647. PubMed ID: 29845012
[TBL] [Abstract][Full Text] [Related]
13. Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images.
Li H; Jiang G; Zhang J; Wang R; Wang Z; Zheng WS; Menze B
Neuroimage; 2018 Dec; 183():650-665. PubMed ID: 30125711
[TBL] [Abstract][Full Text] [Related]
14. Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation.
Mojiri Forooshani P; Biparva M; Ntiri EE; Ramirez J; Boone L; Holmes MF; Adamo S; Gao F; Ozzoude M; Scott CJM; Dowlatshahi D; Lawrence-Dewar JM; Kwan D; Lang AE; Marcotte K; Leonard C; Rochon E; Heyn C; Bartha R; Strother S; Tardif JC; Symons S; Masellis M; Swartz RH; Moody A; Black SE; Goubran M
Hum Brain Mapp; 2022 May; 43(7):2089-2108. PubMed ID: 35088930
[TBL] [Abstract][Full Text] [Related]
15. Early detection of white matter hyperintensities using SHIVA-WMH detector.
Tsuchida A; Boutinaud P; Verrecchia V; Tzourio C; Debette S; Joliot M
Hum Brain Mapp; 2024 Jan; 45(1):e26548. PubMed ID: 38050769
[TBL] [Abstract][Full Text] [Related]
16. Improved Automatic Segmentation of White Matter Hyperintensities in MRI Based on Multilevel Lesion Features.
Rincón M; Díaz-López E; Selnes P; Vegge K; Altmann M; Fladby T; Bjørnerud A
Neuroinformatics; 2017 Jul; 15(3):231-245. PubMed ID: 28378263
[TBL] [Abstract][Full Text] [Related]
17. Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology.
Rachmadi MF; Valdés-Hernández MDC; Agan MLF; Di Perri C; Komura T;
Comput Med Imaging Graph; 2018 Jun; 66():28-43. PubMed ID: 29523002
[TBL] [Abstract][Full Text] [Related]
18. Three-tissue compositional analysis reveals in-vivo microstructural heterogeneity of white matter hyperintensities following stroke.
Khan W; Egorova N; Khlif MS; Mito R; Dhollander T; Brodtmann A
Neuroimage; 2020 Sep; 218():116869. PubMed ID: 32334092
[TBL] [Abstract][Full Text] [Related]
19. Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging.
Dadar M; Maranzano J; Misquitta K; Anor CJ; Fonov VS; Tartaglia MC; Carmichael OT; Decarli C; Collins DL;
Neuroimage; 2017 Aug; 157():233-249. PubMed ID: 28602597
[TBL] [Abstract][Full Text] [Related]
20. White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks.
Guerrero R; Qin C; Oktay O; Bowles C; Chen L; Joules R; Wolz R; Valdés-Hernández MC; Dickie DA; Wardlaw J; Rueckert D
Neuroimage Clin; 2018; 17():918-934. PubMed ID: 29527496
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
