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Items: 7

1.

A generative model for image segmentation based on label fusion.

Sabuncu MR, Yeo BT, Van Leemput K, Fischl B, Golland P.

IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. doi: 10.1109/TMI.2010.2050897. Epub 2010 Jun 17.

2.

Segmentation of joint and musculoskeletal tissue in the study of arthritis.

Pedoia V, Majumdar S, Link TM.

MAGMA. 2016 Apr;29(2):207-21. doi: 10.1007/s10334-016-0532-9. Epub 2016 Feb 25. Review.

PMID:
26915082
3.

Multi-atlas segmentation of biomedical images: A survey.

Iglesias JE, Sabuncu MR.

Med Image Anal. 2015 Aug;24(1):205-219. doi: 10.1016/j.media.2015.06.012. Epub 2015 Jul 6. Review.

4.

Fully automated whole-head segmentation with improved smoothness and continuity, with theory reviewed.

Huang Y, Parra LC.

PLoS One. 2015 May 18;10(5):e0125477. doi: 10.1371/journal.pone.0125477. eCollection 2015. Review.

5.

Multivariate models of inter-subject anatomical variability.

Ashburner J, Klöppel S.

Neuroimage. 2011 May 15;56(2):422-39. doi: 10.1016/j.neuroimage.2010.03.059. Epub 2010 Mar 27. Review.

6.

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.

Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ.

J Digit Imaging. 2017 Aug;30(4):449-459. doi: 10.1007/s10278-017-9983-4. Review.

7.

A review on automatic fetal and neonatal brain MRI segmentation.

Makropoulos A, Counsell SJ, Rueckert D.

Neuroimage. 2018 Apr 15;170:231-248. doi: 10.1016/j.neuroimage.2017.06.074. Epub 2017 Jun 28. Review.

PMID:
28666878

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