441 related articles for article (PubMed ID: 30300751)
1. 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]
2. Automated Cervical Spinal Cord Segmentation in Real-World MRI of Multiple Sclerosis Patients by Optimized Hybrid Residual Attention-Aware Convolutional Neural Networks.
Bueno A; Bosch I; Rodríguez A; Jiménez A; Carreres J; Fernández M; Marti-Bonmati L; Alberich-Bayarri A
J Digit Imaging; 2022 Oct; 35(5):1131-1142. PubMed ID: 35789447
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. 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]
6. 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]
7. Fully-integrated framework for the segmentation and registration of the spinal cord white and gray matter.
Dupont SM; De Leener B; Taso M; Le Troter A; Nadeau S; Stikov N; Callot V; Cohen-Adad J
Neuroimage; 2017 Apr; 150():358-372. PubMed ID: 27663988
[TBL] [Abstract][Full Text] [Related]
8. Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.
Gaj S; Ontaneda D; Nakamura K
PLoS One; 2021; 16(9):e0255939. PubMed ID: 34469432
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. Gray matter segmentation of the spinal cord with active contours in MR images.
Datta E; Papinutto N; Schlaeger R; Zhu A; Carballido-Gamio J; Henry RG
Neuroimage; 2017 Feb; 147():788-799. PubMed ID: 27495383
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. Fully automated segmentation of the cervical cord from T1-weighted MRI using PropSeg: Application to multiple sclerosis.
Yiannakas MC; Mustafa AM; De Leener B; Kearney H; Tur C; Altmann DR; De Angelis F; Plantone D; Ciccarelli O; Miller DH; Cohen-Adad J; Gandini Wheeler-Kingshott CA
Neuroimage Clin; 2016; 10():71-7. PubMed ID: 26793433
[TBL] [Abstract][Full Text] [Related]
15. Convolutional Neural Network-Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury.
McCoy DB; Dupont SM; Gros C; Cohen-Adad J; Huie RJ; Ferguson A; Duong-Fernandez X; Thomas LH; Singh V; Narvid J; Pascual L; Kyritsis N; Beattie MS; Bresnahan JC; Dhall S; Whetstone W; Talbott JF;
AJNR Am J Neuroradiol; 2019 Apr; 40(4):737-744. PubMed ID: 30923086
[TBL] [Abstract][Full Text] [Related]
16. Robust, accurate and fast automatic segmentation of the spinal cord.
De Leener B; Kadoury S; Cohen-Adad J
Neuroimage; 2014 Sep; 98():528-36. PubMed ID: 24780696
[TBL] [Abstract][Full Text] [Related]
17. Automatic spinal cord localization, robust to MRI contrasts using global curve optimization.
Gros C; De Leener B; Dupont SM; Martin AR; Fehlings MG; Bakshi R; Tummala S; Auclair V; McLaren DG; Callot V; Cohen-Adad J; Sdika M
Med Image Anal; 2018 Feb; 44():215-227. PubMed ID: 29288983
[TBL] [Abstract][Full Text] [Related]
18. Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning.
Narayana PA; Coronado I; Sujit SJ; Sun X; Wolinsky JS; Gabr RE
Magn Reson Imaging; 2020 Jan; 65():8-14. PubMed ID: 31670238
[TBL] [Abstract][Full Text] [Related]
19. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images.
Jain S; Sima DM; Ribbens A; Cambron M; Maertens A; Van Hecke W; De Mey J; Barkhof F; Steenwijk MD; Daams M; Maes F; Van Huffel S; Vrenken H; Smeets D
Neuroimage Clin; 2015; 8():367-75. PubMed ID: 26106562
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
