200 related articles for article (PubMed ID: 36507318)
1. Improving the detection of new lesions in multiple sclerosis with a cascaded 3D fully convolutional neural network approach.
Salem M; Ryan MA; Oliver A; Hussain KF; Lladó X
Front Neurosci; 2022; 16():1007619. PubMed ID: 36507318
[TBL] [Abstract][Full Text] [Related]
2. A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis.
Salem M; Valverde S; Cabezas M; Pareto D; Oliver A; Salvi J; Rovira À; Lladó X
Neuroimage Clin; 2020; 25():102149. PubMed ID: 31918065
[TBL] [Abstract][Full Text] [Related]
3. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach.
Valverde S; Cabezas M; Roura E; González-Villà S; Pareto D; Vilanova JC; Ramió-Torrentà L; Rovira À; Oliver A; Lladó X
Neuroimage; 2017 Jul; 155():159-168. PubMed ID: 28435096
[TBL] [Abstract][Full Text] [Related]
4. Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks.
Krüger J; Opfer R; Gessert N; Ostwaldt AC; Manogaran P; Kitzler HH; Schlaefer A; Schippling S
Neuroimage Clin; 2020; 28():102445. PubMed ID: 33038667
[TBL] [Abstract][Full Text] [Related]
5. Infratentorial lesions in multiple sclerosis patients: intra- and inter-rater variability in comparison to a fully automated segmentation using 3D convolutional neural networks.
Krüger J; Ostwaldt AC; Spies L; Geisler B; Schlaefer A; Kitzler HH; Schippling S; Opfer R
Eur Radiol; 2022 Apr; 32(4):2798-2809. PubMed ID: 34643779
[TBL] [Abstract][Full Text] [Related]
6. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.
Gros C; De Leener B; Badji A; Maranzano J; Eden D; Dupont SM; Talbott J; Zhuoquiong R; Liu Y; Granberg T; Ouellette R; Tachibana Y; Hori M; Kamiya K; Chougar L; Stawiarz L; Hillert J; Bannier E; Kerbrat A; Edan G; Labauge P; Callot V; Pelletier J; Audoin B; Rasoanandrianina H; Brisset JC; Valsasina P; Rocca MA; Filippi M; Bakshi R; Tauhid S; Prados F; Yiannakas M; Kearney H; Ciccarelli O; Smith S; Treaba CA; Mainero C; Lefeuvre J; Reich DS; Nair G; Auclair V; McLaren DG; Martin AR; Fehlings MG; Vahdat S; Khatibi A; Doyon J; Shepherd T; Charlson E; Narayanan S; Cohen-Adad J
Neuroimage; 2019 Jan; 184():901-915. PubMed ID: 30300751
[TBL] [Abstract][Full Text] [Related]
7. Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection.
Hashemi SR; Salehi SSM; Erdogmus D; Prabhu SP; Warfield SK; Gholipour A
IEEE Access; 2019; 7():721-1735. PubMed ID: 31528523
[TBL] [Abstract][Full Text] [Related]
8. Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability.
Opfer R; Krüger J; Spies L; Ostwaldt AC; Kitzler HH; Schippling S; Buchert R
Eur Radiol; 2023 Mar; 33(3):1852-1861. PubMed ID: 36264314
[TBL] [Abstract][Full Text] [Related]
9. Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis.
Brugnara G; Isensee F; Neuberger U; Bonekamp D; Petersen J; Diem R; Wildemann B; Heiland S; Wick W; Bendszus M; Maier-Hein K; Kickingereder P
Eur Radiol; 2020 Apr; 30(4):2356-2364. PubMed ID: 31900702
[TBL] [Abstract][Full Text] [Related]
10. RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis.
Barquero G; La Rosa F; Kebiri H; Lu PJ; Rahmanzadeh R; Weigel M; Fartaria MJ; Kober T; Théaudin M; Du Pasquier R; Sati P; Reich DS; Absinta M; Granziera C; Maggi P; Bach Cuadra M
Neuroimage Clin; 2020; 28():102412. PubMed ID: 32961401
[TBL] [Abstract][Full Text] [Related]
11. Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation.
Essa E; Aldesouky D; Hussein SE; Rashad MZ
Med Biol Eng Comput; 2020 Sep; 58(9):2161-2175. PubMed ID: 32681214
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. A dense residual U-net for multiple sclerosis lesions segmentation from multi-sequence 3D MR images.
Sarica B; Seker DZ; Bayram B
Int J Med Inform; 2023 Feb; 170():104965. PubMed ID: 36580821
[TBL] [Abstract][Full Text] [Related]
14. Automatic localization and segmentation of focal cortical dysplasia in FLAIR-negative patients using a convolutional neural network.
Feng C; Zhao H; Li Y; Wen J
J Appl Clin Med Phys; 2020 Sep; 21(9):215-226. PubMed ID: 32809276
[TBL] [Abstract][Full Text] [Related]
15. QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps.
Zhang H; Nguyen TD; Zhang J; Marcille M; Spincemaille P; Wang Y; Gauthier SA; Sweeney EM
Neuroimage Clin; 2022; 34():102979. PubMed ID: 35247730
[TBL] [Abstract][Full Text] [Related]
16. New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images.
Sarica B; Seker DZ
Front Neurosci; 2022; 16():912000. PubMed ID: 35968389
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI.
Roca P; Attye A; Colas L; Tucholka A; Rubini P; Cackowski S; Ding J; Budzik JF; Renard F; Doyle S; Barbier EL; Bousaid I; Casey R; Vukusic S; Lassau N; Verclytte S; Cotton F; ; ; ;
Diagn Interv Imaging; 2020 Dec; 101(12):795-802. PubMed ID: 32651155
[TBL] [Abstract][Full Text] [Related]
19. Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study.
Gabr RE; Coronado I; Robinson M; Sujit SJ; Datta S; Sun X; Allen WJ; Lublin FD; Wolinsky JS; Narayana PA
Mult Scler; 2020 Sep; 26(10):1217-1226. PubMed ID: 31190607
[TBL] [Abstract][Full Text] [Related]
20. One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks.
Valverde S; Salem M; Cabezas M; Pareto D; Vilanova JC; Ramió-Torrentà L; Rovira À; Salvi J; Oliver A; Lladó X
Neuroimage Clin; 2019; 21():101638. PubMed ID: 30555005
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
