Atlas-based registration parameters in segmenting sub-cortical regions from brain MRI-images

Jyrki Lötjönen, Juha Koikkalainen, L. Thurfjell, D. Rueckert

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

    5 Citations (Scopus)

    Abstract

    Multi-atlas segmentation has been proved to perform well in segmenting sub-cortical structures from images. In this work, we study different components of multi-atlas segmentation and propose new techniques to improve the segmentation accuracy. We found that the use of gradient information in addition to standard normalised mutual information increases the registration accuracy. We also studied different techniques to select atlases in the multi-atlas segmentation. In addition, the expectation maximisation algorithm was used to combine multi-atlas and intensity model information. The average similarity index obtained for six subcortical structures was 0.84. (11 refs.)
    Original languageEnglish
    Title of host publication2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
    Place of PublicationPiscataway
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages21-24
    ISBN (Print)978-1-4244-3931-7
    DOIs
    Publication statusPublished - 2009
    MoE publication typeA4 Article in a conference publication
    EventIEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Boston, United States
    Duration: 28 Jun 20091 Jul 2009

    Conference

    ConferenceIEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
    CountryUnited States
    CityBoston
    Period28/06/091/07/09

    Fingerprint

    Magnetic resonance imaging
    Brain

    Keywords

    • Atlases
    • brain
    • registation
    • segmentation

    Cite this

    Lötjönen, J., Koikkalainen, J., Thurfjell, L., & Rueckert, D. (2009). Atlas-based registration parameters in segmenting sub-cortical regions from brain MRI-images. In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI (pp. 21-24). Piscataway: IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/ISBI.2009.5192973
    Lötjönen, Jyrki ; Koikkalainen, Juha ; Thurfjell, L. ; Rueckert, D. / Atlas-based registration parameters in segmenting sub-cortical regions from brain MRI-images. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI. Piscataway : IEEE Institute of Electrical and Electronic Engineers , 2009. pp. 21-24
    @inproceedings{e994501d490b4047b81e26c857e9c508,
    title = "Atlas-based registration parameters in segmenting sub-cortical regions from brain MRI-images",
    abstract = "Multi-atlas segmentation has been proved to perform well in segmenting sub-cortical structures from images. In this work, we study different components of multi-atlas segmentation and propose new techniques to improve the segmentation accuracy. We found that the use of gradient information in addition to standard normalised mutual information increases the registration accuracy. We also studied different techniques to select atlases in the multi-atlas segmentation. In addition, the expectation maximisation algorithm was used to combine multi-atlas and intensity model information. The average similarity index obtained for six subcortical structures was 0.84. (11 refs.)",
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    author = "Jyrki L{\"o}tj{\"o}nen and Juha Koikkalainen and L. Thurfjell and D. Rueckert",
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    doi = "10.1109/ISBI.2009.5192973",
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    Lötjönen, J, Koikkalainen, J, Thurfjell, L & Rueckert, D 2009, Atlas-based registration parameters in segmenting sub-cortical regions from brain MRI-images. in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI. IEEE Institute of Electrical and Electronic Engineers , Piscataway, pp. 21-24, IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI, Boston, United States, 28/06/09. https://doi.org/10.1109/ISBI.2009.5192973

    Atlas-based registration parameters in segmenting sub-cortical regions from brain MRI-images. / Lötjönen, Jyrki; Koikkalainen, Juha; Thurfjell, L.; Rueckert, D.

    2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI. Piscataway : IEEE Institute of Electrical and Electronic Engineers , 2009. p. 21-24.

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

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    AU - Rueckert, D.

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    N2 - Multi-atlas segmentation has been proved to perform well in segmenting sub-cortical structures from images. In this work, we study different components of multi-atlas segmentation and propose new techniques to improve the segmentation accuracy. We found that the use of gradient information in addition to standard normalised mutual information increases the registration accuracy. We also studied different techniques to select atlases in the multi-atlas segmentation. In addition, the expectation maximisation algorithm was used to combine multi-atlas and intensity model information. The average similarity index obtained for six subcortical structures was 0.84. (11 refs.)

    AB - Multi-atlas segmentation has been proved to perform well in segmenting sub-cortical structures from images. In this work, we study different components of multi-atlas segmentation and propose new techniques to improve the segmentation accuracy. We found that the use of gradient information in addition to standard normalised mutual information increases the registration accuracy. We also studied different techniques to select atlases in the multi-atlas segmentation. In addition, the expectation maximisation algorithm was used to combine multi-atlas and intensity model information. The average similarity index obtained for six subcortical structures was 0.84. (11 refs.)

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    Lötjönen J, Koikkalainen J, Thurfjell L, Rueckert D. Atlas-based registration parameters in segmenting sub-cortical regions from brain MRI-images. In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI. Piscataway: IEEE Institute of Electrical and Electronic Engineers . 2009. p. 21-24 https://doi.org/10.1109/ISBI.2009.5192973