191 related articles for article (PubMed ID: 26432931)
1. Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans.
Griffis JC; Allendorfer JB; Szaflarski JP
J Neurosci Methods; 2016 Jan; 257():97-108. PubMed ID: 26432931
[TBL] [Abstract][Full Text] [Related]
2. Automated lesion detection on MRI scans using combined unsupervised and supervised methods.
Guo D; Fridriksson J; Fillmore P; Rorden C; Yu H; Zheng K; Wang S
BMC Med Imaging; 2015 Oct; 15():50. PubMed ID: 26518734
[TBL] [Abstract][Full Text] [Related]
3. Lesion segmentation from multimodal MRI using random forest following ischemic stroke.
Mitra J; Bourgeat P; Fripp J; Ghose S; Rose S; Salvado O; Connelly A; Campbell B; Palmer S; Sharma G; Christensen S; Carey L
Neuroimage; 2014 Sep; 98():324-35. PubMed ID: 24793830
[TBL] [Abstract][Full Text] [Related]
4. Machine learning identifies stroke features between species.
Castaneda-Vega S; Katiyar P; Russo F; Patzwaldt K; Schnabel L; Mathes S; Hempel JM; Kohlhofer U; Gonzalez-Menendez I; Quintanilla-Martinez L; Ziemann U; la Fougere C; Ernemann U; Pichler BJ; Disselhorst JA; Poli S
Theranostics; 2021; 11(6):3017-3034. PubMed ID: 33456586
[TBL] [Abstract][Full Text] [Related]
5. Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences.
Maier O; Wilms M; von der Gablentz J; Krämer UM; Münte TF; Handels H
J Neurosci Methods; 2015 Jan; 240():89-100. PubMed ID: 25448384
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. A comparison of automated lesion segmentation approaches for chronic stroke T1-weighted MRI data.
Ito KL; Kim H; Liew SL
Hum Brain Mapp; 2019 Nov; 40(16):4669-4685. PubMed ID: 31350795
[TBL] [Abstract][Full Text] [Related]
8. Automated segmentation of chronic stroke lesions using LINDA: Lesion identification with neighborhood data analysis.
Pustina D; Coslett HB; Turkeltaub PE; Tustison N; Schwartz MF; Avants B
Hum Brain Mapp; 2016 Apr; 37(4):1405-21. PubMed ID: 26756101
[TBL] [Abstract][Full Text] [Related]
9. FLAIR lesion segmentation: application in patients with brain tumors and acute ischemic stroke.
Artzi M; Aizenstein O; Jonas-Kimchi T; Myers V; Hallevi H; Ben Bashat D
Eur J Radiol; 2013 Sep; 82(9):1512-8. PubMed ID: 23796882
[TBL] [Abstract][Full Text] [Related]
10. RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields.
Chen G; Li Q; Shi F; Rekik I; Pan Z
Neuroimage; 2020 May; 211():116620. PubMed ID: 32057997
[TBL] [Abstract][Full Text] [Related]
11. Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI.
Elliott C; Arnold DL; Collins DL; Arbel T
IEEE Trans Med Imaging; 2013 Aug; 32(8):1490-503. PubMed ID: 23613032
[TBL] [Abstract][Full Text] [Related]
12. Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.
Sauwen N; Acou M; Sima DM; Veraart J; Maes F; Himmelreich U; Achten E; Huffel SV
BMC Med Imaging; 2017 May; 17(1):29. PubMed ID: 28472943
[TBL] [Abstract][Full Text] [Related]
13. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods.
Wilke M; de Haan B; Juenger H; Karnath HO
Neuroimage; 2011 Jun; 56(4):2038-46. PubMed ID: 21513805
[TBL] [Abstract][Full Text] [Related]
14. Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study.
Maier O; Schröder C; Forkert ND; Martinetz T; Handels H
PLoS One; 2015; 10(12):e0145118. PubMed ID: 26672989
[TBL] [Abstract][Full Text] [Related]
15. A comparison of automated segmentation and manual tracing in estimating hippocampal volume in ischemic stroke and healthy control participants.
Khlif MS; Egorova N; Werden E; Redolfi A; Boccardi M; DeCarli CS; Fletcher E; Singh B; Li Q; Bird L; Brodtmann A
Neuroimage Clin; 2019; 21():101581. PubMed ID: 30606656
[TBL] [Abstract][Full Text] [Related]
16. Weighting training images by maximizing distribution similarity for supervised segmentation across scanners.
Opbroek AV; Vernooij MW; Ikram MA; Bruijne M
Med Image Anal; 2015 Aug; 24(1):245-254. PubMed ID: 26210914
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Fast semi-automated lesion demarcation in stroke.
de Haan B; Clas P; Juenger H; Wilke M; Karnath HO
Neuroimage Clin; 2015; 9():69-74. PubMed ID: 26413473
[TBL] [Abstract][Full Text] [Related]
19. Naïve Bayes classifier assisted automated detection of cerebral microbleeds in susceptibility-weighted imaging brain images.
Ateeq T; Faheem ZB; Ghoneimy M; Ali J; Li Y; Baz A
Biochem Cell Biol; 2023 Dec; 101(6):562-573. PubMed ID: 37639730
[TBL] [Abstract][Full Text] [Related]
20. Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI.
Subudhi A; Jena S; Sabut S
Med Biol Eng Comput; 2018 May; 56(5):795-807. PubMed ID: 28948480
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
