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Items: 1 to 20 of 99

1.

Brain structure segmentation in the presence of multiple sclerosis lesions.

González-Villà S, Oliver A, Huo Y, Lladó X, Landman BA.

Neuroimage Clin. 2019 Feb 14;22:101709. doi: 10.1016/j.nicl.2019.101709. [Epub ahead of print]

2.

Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion.

Dong M, Oguz I, Subbana N, Calabresi P, Shinohara RT, Yushkevich P.

Patch Based Tech Med Imaging (2017). 2017 Sep;10530:138-145. doi: 10.1007/978-3-319-67434-6_16. Epub 2017 Aug 31.

3.

Evaluation of Multi-Atlas Label Fusion for In Vivo MRI Orbital Segmentation.

Panda S, Asman AJ, Khare SP, Thompson L, Mawn LA, Smith SA, Landman BA.

J Med Imaging (Bellingham). 2014 Jul 18;1(2). pii: 024002.

4.

Discriminative confidence estimation for probabilistic multi-atlas label fusion.

Benkarim OM, Piella G, González Ballester MA, Sanroma G; Alzheimer’s Disease Neuroimaging Initiative.

Med Image Anal. 2017 Dec;42:274-287. doi: 10.1016/j.media.2017.08.008. Epub 2017 Sep 1.

PMID:
28888171
5.

Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation.

González-Villà S, Valverde S, Cabezas M, Pareto D, Vilanova JC, Ramió-Torrentà L, Rovira À, Oliver A, Lladó X.

Neuroimage Clin. 2017 May 8;15:228-238. doi: 10.1016/j.nicl.2017.05.003. eCollection 2017.

6.

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; Alzheimer's Disease Neuroimaging Initiative.

Neuroimage. 2015 Feb 1;106:34-46. doi: 10.1016/j.neuroimage.2014.11.025. Epub 2014 Nov 20.

7.

Comparison of atlas-based techniques for whole-body bone segmentation.

Arabi H, Zaidi H.

Med Image Anal. 2017 Feb;36:98-112. doi: 10.1016/j.media.2016.11.003. Epub 2016 Nov 12.

PMID:
27871000
8.

Example Based Lesion Segmentation.

Roy S, He Q, Carass A, Jog A, Cuzzocreo JL, Reich DS, Prince J, Pham D.

Proc SPIE Int Soc Opt Eng. 2014 Feb 15;9034. pii: 90341Y. Epub 2014 Mar 21.

9.

Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation.

Fleishman GM, Valcarcel A, Pham DL, Roy S, Calabresi PA, Yushkevich P, Shinohara RT, Oguz I.

Brainlesion (2017). 2018;10670:43-54. Epub 2018 Feb 17.

10.

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. doi: 10.1016/j.compmedimag.2018.11.004. Epub 2018 Dec 6.

PMID:
30553173
11.

Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion.

Ma D, Cardoso MJ, Modat M, Powell N, Wells J, Holmes H, Wiseman F, Tybulewicz V, Fisher E, Lythgoe MF, Ourselin S.

PLoS One. 2014 Jan 27;9(1):e86576. doi: 10.1371/journal.pone.0086576. eCollection 2014.

12.

Low-complexity atlas-based prostate segmentation by combining global, regional, and local metrics.

Xie Q, Ruan D.

Med Phys. 2014 Apr;41(4):041909. doi: 10.1118/1.4867855.

PMID:
24694140
13.

4D Multi-atlas Label Fusion using Longitudinal Images.

Huo Y, Resnick SM, Landman BA.

Patch Based Tech Med Imaging (2017). 2017;10530:3-11. doi: 10.1007/978-3-319-67434-6_1. Epub 2017 Aug 31.

14.

Multi-atlas label fusion using hybrid of discriminative and generative classifiers for segmentation of cardiac MR images.

Sedai S, Garnavi R, Roy P, Xi Liang.

Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:2977-80. doi: 10.1109/EMBC.2015.7319017.

PMID:
26736917
15.

WE-E-213CD-06: A Locally Adaptive, Intensity-Based Label Fusion Method for Multi- Atlas Auto-Segmentation.

Han X.

Med Phys. 2012 Jun;39(6Part27):3960. doi: 10.1118/1.4736162.

PMID:
28520018
16.

Multi-Atlas Segmentation with Joint Label Fusion.

Wang H, Suh JW, Das SR, Pluta JB, Craige C, Yushkevich PA.

IEEE Trans Pattern Anal Mach Intell. 2013 Mar;35(3):611-23. doi: 10.1109/TPAMI.2012.143. Epub 2012 Jun 26.

17.

Rotation-invariant multi-contrast non-local means for MS lesion segmentation.

Guizard N, Coupé P, Fonov VS, Manjón JV, Arnold DL, Collins DL.

Neuroimage Clin. 2015 May 13;8:376-89. doi: 10.1016/j.nicl.2015.05.001. eCollection 2015.

18.

Optimal number of atlases and label fusion for automatic multi-atlas-based brachial plexus contouring in radiotherapy treatment planning.

Van de Velde J, Wouters J, Vercauteren T, De Gersem W, Achten E, De Neve W, Van Hoof T.

Radiat Oncol. 2016 Jan 7;11:1. doi: 10.1186/s13014-015-0579-1.

19.

Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection.

Zhuang X, Bai W, Song J, Zhan S, Qian X, Shi W, Lian Y, Rueckert D.

Med Phys. 2015 Jul;42(7):3822-33. doi: 10.1118/1.4921366.

PMID:
26133584
20.

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 1;101:494-512. doi: 10.1016/j.neuroimage.2014.04.054. Epub 2014 Apr 29.

PMID:
24784800

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