356 related articles for article (PubMed ID: 24151008)
1. Local label learning (LLL) for subcortical structure segmentation: application to hippocampus segmentation.
Hao Y; Wang T; Zhang X; Duan Y; Yu C; Jiang T; Fan Y;
Hum Brain Mapp; 2014 Jun; 35(6):2674-97. PubMed ID: 24151008
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Patch-Based Label Fusion with Structured Discriminant Embedding for Hippocampus Segmentation.
Wang Y; Ma G; Wu X; Zhou J
Neuroinformatics; 2018 Oct; 16(3-4):411-423. PubMed ID: 29512026
[TBL] [Abstract][Full Text] [Related]
4. Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation.
Zhu H; Tang Z; Cheng H; Wu Y; Fan Y
Sci Rep; 2019 Nov; 9(1):16839. PubMed ID: 31727982
[TBL] [Abstract][Full Text] [Related]
5. Metric Learning for Multi-atlas based Segmentation of Hippocampus.
Zhu H; Cheng H; Yang X; Fan Y;
Neuroinformatics; 2017 Jan; 15(1):41-50. PubMed ID: 27638650
[TBL] [Abstract][Full Text] [Related]
6. Local manifold learning for multiatlas segmentation: application to hippocampal segmentation in healthy population and Alzheimer's disease.
Li XW; Li QL; Li SY; Li DY;
CNS Neurosci Ther; 2015 Oct; 21(10):826-36. PubMed ID: 26122409
[TBL] [Abstract][Full Text] [Related]
7. View-centralized multi-atlas classification for Alzheimer's disease diagnosis.
Liu M; Zhang D; Shen D;
Hum Brain Mapp; 2015 May; 36(5):1847-65. PubMed ID: 25624081
[TBL] [Abstract][Full Text] [Related]
8. A label fusion method using conditional random fields with higher-order potentials: Application to hippocampal segmentation.
Platero C; Carmen Tobar M
Artif Intell Med; 2015 Jun; 64(2):117-29. PubMed ID: 25982908
[TBL] [Abstract][Full Text] [Related]
9. A learning-based wrapper method to correct systematic errors in automatic image segmentation: consistently improved performance in hippocampus, cortex and brain segmentation.
Wang H; Das SR; Suh JW; Altinay M; Pluta J; Craige C; Avants B; Yushkevich PA;
Neuroimage; 2011 Apr; 55(3):968-85. PubMed ID: 21237273
[TBL] [Abstract][Full Text] [Related]
10. Discriminative confidence estimation for probabilistic multi-atlas label fusion.
Benkarim OM; Piella G; González Ballester MA; Sanroma G;
Med Image Anal; 2017 Dec; 42():274-287. PubMed ID: 28888171
[TBL] [Abstract][Full Text] [Related]
11. Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition.
Wu G; Kim M; Sanroma G; Wang Q; Munsell BC; Shen D;
Neuroimage; 2015 Feb; 106():34-46. PubMed ID: 25463474
[TBL] [Abstract][Full Text] [Related]
12. Automated segmentation of mouse brain images using multi-atlas multi-ROI deformation and label fusion.
Nie J; Shen D
Neuroinformatics; 2013 Jan; 11(1):35-45. PubMed ID: 23055043
[TBL] [Abstract][Full Text] [Related]
13. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI.
Iglesias JE; Augustinack JC; Nguyen K; Player CM; Player A; Wright M; Roy N; Frosch MP; McKee AC; Wald LL; Fischl B; Van Leemput K;
Neuroimage; 2015 Jul; 115():117-37. PubMed ID: 25936807
[TBL] [Abstract][Full Text] [Related]
14. Reliability-based robust multi-atlas label fusion for brain MRI segmentation.
Sun L; Zu C; Shao W; Guang J; Zhang D; Liu M
Artif Intell Med; 2019 May; 96():12-24. PubMed ID: 31164205
[TBL] [Abstract][Full Text] [Related]
15. STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation.
Jorge Cardoso M; Leung K; Modat M; Keihaninejad S; Cash D; Barnes J; Fox NC; Ourselin S;
Med Image Anal; 2013 Aug; 17(6):671-84. PubMed ID: 23510558
[TBL] [Abstract][Full Text] [Related]
16. FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation.
Zhu H; Adeli E; Shi F; Shen D;
Neuroinformatics; 2020 Apr; 18(2):319-331. PubMed ID: 31898145
[TBL] [Abstract][Full Text] [Related]
17. Patch spaces and fusion strategies in patch-based label fusion.
Benkarim OM; Piella G; Hahner N; Eixarch E; González Ballester MA; Sanroma G
Comput Med Imaging Graph; 2019 Jan; 71():79-89. PubMed ID: 30553173
[TBL] [Abstract][Full Text] [Related]
18. A unified framework for cross-modality multi-atlas segmentation of brain MRI.
Eugenio Iglesias J; Rory Sabuncu M; Van Leemput K
Med Image Anal; 2013 Dec; 17(8):1181-91. PubMed ID: 24001931
[TBL] [Abstract][Full Text] [Related]
19. MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection.
Doshi J; Erus G; Ou Y; Resnick SM; Gur RC; Gur RE; Satterthwaite TD; Furth S; Davatzikos C;
Neuroimage; 2016 Feb; 127():186-195. PubMed ID: 26679328
[TBL] [Abstract][Full Text] [Related]
20. Using manifold learning for atlas selection in multi-atlas segmentation.
Hoang Duc AK; Modat M; Leung KK; Cardoso MJ; Barnes J; Kadir T; Ourselin S;
PLoS One; 2013; 8(8):e70059. PubMed ID: 23936376
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
