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Journal Abstract Search

199 related items for 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 15; 257():97-108. PubMed ID: 26432931
    [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 30; 15():50. PubMed ID: 26518734
    [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 30; 98():324-35. PubMed ID: 24793830
    [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 Sep 30; 11(6):3017-3034. PubMed ID: 33456586
    [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 30; 240():89-100. PubMed ID: 25448384
    [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 Jan 30; 25():102118. PubMed ID: 31865021
    [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 01; 40(16):4669-4685. PubMed ID: 31350795
    [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 01; 37(4):1405-21. PubMed ID: 26756101
    [Abstract] [Full Text] [Related]

  • 9. Segmentation of stroke lesions using transformers-augmented MRI analysis.
    Ahmed R, Al Shehhi A, Werghi N, Seghier ML.
    Hum Brain Mapp; 2024 Aug 01; 45(11):e26803. PubMed ID: 39119860
    [Abstract] [Full Text] [Related]

  • 10. 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 01; 82(9):1512-8. PubMed ID: 23796882
    [Abstract] [Full Text] [Related]

  • 11. 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 01; 211():116620. PubMed ID: 32057997
    [Abstract] [Full Text] [Related]

  • 12. 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 01; 32(8):1490-503. PubMed ID: 23613032
    [Abstract] [Full Text] [Related]

  • 13. 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 04; 17(1):29. PubMed ID: 28472943
    [Abstract] [Full Text] [Related]

  • 14. 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 15; 56(4):2038-46. PubMed ID: 21513805
    [Abstract] [Full Text] [Related]

  • 15. Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study.
    Maier O, Schröder C, Forkert ND, Martinetz T, Handels H.
    PLoS One; 2015 Jun 15; 10(12):e0145118. PubMed ID: 26672989
    [Abstract] [Full Text] [Related]

  • 16. 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 Jun 15; 21():101581. PubMed ID: 30606656
    [Abstract] [Full Text] [Related]

  • 17. 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 15; 24(1):245-254. PubMed ID: 26210914
    [Abstract] [Full Text] [Related]

  • 18. 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, Alzheimer's Disease Neuroimaging Initiative.
    Neuroimage; 2014 Nov 01; 101():494-512. PubMed ID: 24784800
    [Abstract] [Full Text] [Related]

  • 19. Fast semi-automated lesion demarcation in stroke.
    de Haan B, Clas P, Juenger H, Wilke M, Karnath HO.
    Neuroimage Clin; 2015 Nov 01; 9():69-74. PubMed ID: 26413473
    [Abstract] [Full Text] [Related]

  • 20. 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 01; 101(6):562-573. PubMed ID: 37639730
    [Abstract] [Full Text] [Related]

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