Improved generation of probabilistic atlases for the expectation maximization classification

Jyrki Lötjönen, Robin Wolz, Juha Koikkalainen, Lennart Thurfjell, Roger Lundqvist, Gunhild Waldemar, Hilkka Soininen, Daniel Rueckert

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

7 Citations (Scopus)

Abstract

Probabilistic atlases present prior knowledge about the spatial distribution of various structures or tissues in a population, used commonly in segmentation. We propose three methods for generating probabilistic atlases: 1) the atlases are constructed in a template space using dense non-rigid transformations and transformed to the space of unseen data, 2) as the method 1 but atlas selection is performed in addition, and 3) the atlases are constructed directly in the space of the unseen data. The methods were evaluated in the segmentation of hippocampus from 340 ADNI cases. When comparing with manual segmentations, the Dice similarity indices were 0.84, 0.85 and 0.87 and the correlation coefficients for the volumes were 0.84, 0.92 and 0.96 for the three methods tested. Our results show clearly the importance of probabilistic atlases in segmentation.
Original languageEnglish
Title of host publicationProceedings
Subtitle of host publication8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011
Place of PublicationPiscataway, NJ, USA
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages1839-1842
ISBN (Electronic)978-1-4244-4128-0
ISBN (Print)978-1-4244-4127-3
DOIs
Publication statusPublished - 2011
MoE publication typeA4 Article in a conference publication
Event8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011 - Chicago, IL, United States
Duration: 30 Mar 20112 Apr 2011

Conference

Conference8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011
Abbreviated titleISBI 2011
CountryUnited States
CityChicago, IL
Period30/03/112/04/11

Fingerprint

atlas
segmentation
similarity index
spatial distribution
method

Keywords

  • segmentation
  • atlas
  • brain

Cite this

Lötjönen, J., Wolz, R., Koikkalainen, J., Thurfjell, L., Lundqvist, R., Waldemar, G., ... Rueckert, D. (2011). Improved generation of probabilistic atlases for the expectation maximization classification. In Proceedings: 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011 (pp. 1839-1842). Piscataway, NJ, USA: Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/ISBI.2011.5872765
Lötjönen, Jyrki ; Wolz, Robin ; Koikkalainen, Juha ; Thurfjell, Lennart ; Lundqvist, Roger ; Waldemar, Gunhild ; Soininen, Hilkka ; Rueckert, Daniel. / Improved generation of probabilistic atlases for the expectation maximization classification. Proceedings: 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011. Piscataway, NJ, USA : Institute of Electrical and Electronic Engineers IEEE, 2011. pp. 1839-1842
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Lötjönen, J, Wolz, R, Koikkalainen, J, Thurfjell, L, Lundqvist, R, Waldemar, G, Soininen, H & Rueckert, D 2011, Improved generation of probabilistic atlases for the expectation maximization classification. in Proceedings: 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011. Institute of Electrical and Electronic Engineers IEEE, Piscataway, NJ, USA, pp. 1839-1842, 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, Chicago, IL, United States, 30/03/11. https://doi.org/10.1109/ISBI.2011.5872765

Improved generation of probabilistic atlases for the expectation maximization classification. / Lötjönen, Jyrki; Wolz, Robin; Koikkalainen, Juha; Thurfjell, Lennart; Lundqvist, Roger; Waldemar, Gunhild; Soininen, Hilkka; Rueckert, Daniel.

Proceedings: 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011. Piscataway, NJ, USA : Institute of Electrical and Electronic Engineers IEEE, 2011. p. 1839-1842.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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AU - Wolz, Robin

AU - Koikkalainen, Juha

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AU - Lundqvist, Roger

AU - Waldemar, Gunhild

AU - Soininen, Hilkka

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N2 - Probabilistic atlases present prior knowledge about the spatial distribution of various structures or tissues in a population, used commonly in segmentation. We propose three methods for generating probabilistic atlases: 1) the atlases are constructed in a template space using dense non-rigid transformations and transformed to the space of unseen data, 2) as the method 1 but atlas selection is performed in addition, and 3) the atlases are constructed directly in the space of the unseen data. The methods were evaluated in the segmentation of hippocampus from 340 ADNI cases. When comparing with manual segmentations, the Dice similarity indices were 0.84, 0.85 and 0.87 and the correlation coefficients for the volumes were 0.84, 0.92 and 0.96 for the three methods tested. Our results show clearly the importance of probabilistic atlases in segmentation.

AB - Probabilistic atlases present prior knowledge about the spatial distribution of various structures or tissues in a population, used commonly in segmentation. We propose three methods for generating probabilistic atlases: 1) the atlases are constructed in a template space using dense non-rigid transformations and transformed to the space of unseen data, 2) as the method 1 but atlas selection is performed in addition, and 3) the atlases are constructed directly in the space of the unseen data. The methods were evaluated in the segmentation of hippocampus from 340 ADNI cases. When comparing with manual segmentations, the Dice similarity indices were 0.84, 0.85 and 0.87 and the correlation coefficients for the volumes were 0.84, 0.92 and 0.96 for the three methods tested. Our results show clearly the importance of probabilistic atlases in segmentation.

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Lötjönen J, Wolz R, Koikkalainen J, Thurfjell L, Lundqvist R, Waldemar G et al. Improved generation of probabilistic atlases for the expectation maximization classification. In Proceedings: 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011. Piscataway, NJ, USA: Institute of Electrical and Electronic Engineers IEEE. 2011. p. 1839-1842 https://doi.org/10.1109/ISBI.2011.5872765