162 related articles for article (PubMed ID: 31927500)
1. Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence.
McKinley R; Wepfer R; Grunder L; Aschwanden F; Fischer T; Friedli C; Muri R; Rummel C; Verma R; Weisstanner C; Wiestler B; Berger C; Eichinger P; Muhlau M; Reyes M; Salmen A; Chan A; Wiest R; Wagner F
Neuroimage Clin; 2020; 25():102104. PubMed ID: 31927500
[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. 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]
6. A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis.
Cerri S; Puonti O; Meier DS; Wuerfel J; Mühlau M; Siebner HR; Van Leemput K
Neuroimage; 2021 Jan; 225():117471. PubMed ID: 33099007
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Accurate and robust segmentation of neuroanatomy in T1-weighted MRI by combining spatial priors with deep convolutional neural networks.
Novosad P; Fonov V; Collins DL;
Hum Brain Mapp; 2020 Feb; 41(2):309-327. PubMed ID: 31633863
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Assessing robustness and generalization of a deep neural network for brain MS lesion segmentation on real-world data.
Chaves H; Serra MM; Shalom DE; Ananía P; Rueda F; Osa Sanz E; Stefanoff NI; Rodríguez Murúa S; Costa ME; Kitamura FC; Yañez P; Cejas C; Correale J; Ferrante E; Fernández Slezak D; Farez MF
Eur Radiol; 2024 Mar; 34(3):2024-2035. PubMed ID: 37650967
[TBL] [Abstract][Full Text] [Related]
11. Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images.
Rachmadi MF; Valdés-Hernández MDC; Li H; Guerrero R; Meijboom R; Wiseman S; Waldman A; Zhang J; Rueckert D; Wardlaw J; Komura T
Comput Med Imaging Graph; 2020 Jan; 79():101685. PubMed ID: 31846826
[TBL] [Abstract][Full Text] [Related]
12. Convolutional Neural Network for Automated FLAIR Lesion Segmentation on Clinical Brain MR Imaging.
Duong MT; Rudie JD; Wang J; Xie L; Mohan S; Gee JC; Rauschecker AM
AJNR Am J Neuroradiol; 2019 Aug; 40(8):1282-1290. PubMed ID: 31345943
[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. Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation.
Nair T; Precup D; Arnold DL; Arbel T
Med Image Anal; 2020 Jan; 59():101557. PubMed ID: 31677438
[TBL] [Abstract][Full Text] [Related]
15. Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.
Brosch T; Tang LY; Youngjin Yoo ; Li DK; Traboulsee A; Tam R
IEEE Trans Med Imaging; 2016 May; 35(5):1229-1239. PubMed ID: 26886978
[TBL] [Abstract][Full Text] [Related]
16. MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions.
Valcarcel AM; Linn KA; Vandekar SN; Satterthwaite TD; Muschelli J; Calabresi PA; Pham DL; Martin ML; Shinohara RT
J Neuroimaging; 2018 Jul; 28(4):389-398. PubMed ID: 29516669
[TBL] [Abstract][Full Text] [Related]
17. Accurate, rapid and reliable, fully automated MRI brainstem segmentation for application in multiple sclerosis and neurodegenerative diseases.
Sander L; Pezold S; Andermatt S; Amann M; Meier D; Wendebourg MJ; Sinnecker T; Radue EW; Naegelin Y; Granziera C; Kappos L; Wuerfel J; Cattin P; Schlaeger R;
Hum Brain Mapp; 2019 Oct; 40(14):4091-4104. PubMed ID: 31206931
[TBL] [Abstract][Full Text] [Related]
18. Automated separation of diffusely abnormal white matter from focal white matter lesions on MRI in multiple sclerosis.
Maranzano J; Dadar M; Zhernovaia M; Arnold DL; Collins DL; Narayanan S
Neuroimage; 2020 Jun; 213():116690. PubMed ID: 32119987
[TBL] [Abstract][Full Text] [Related]
19. A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images.
Xue Y; Farhat FG; Boukrina O; Barrett AM; Binder JR; Roshan UW; Graves WW
Neuroimage Clin; 2020; 25():102118. PubMed ID: 31865021
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
