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Geodesic registration for interactive atlas-based segmentation using learned multi-scale anatomical manifolds
Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Dept. of C4ISR, Swedish Defence Research Agency, Linköping, Sweden, .
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9267-2191
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.ORCID iD: 0000-0002-6189-0807
2018 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 112, p. 340-345Article in journal (Refereed) Published
Abstract [en]

Atlas-based segmentation is often used to segment medical image regions. For intensity-normalized data, the quality of these segmentations is highly dependent on the similarity between the atlas and the target under the used registration method. We propose a geodesic registration method for interactive atlas-based segmentation using empirical multi-scale anatomical manifolds. The method utilizes unlabeled images together with the labeled atlases to learn empirical anatomical manifolds. These manifolds are defined on distinct scales and regions and are used to propagate the labeling information from the atlases to the target along anatomical geodesics. The resulting competing segmentations from the different manifolds are then ranked according to an image-based similarity measure. We used image volumes acquired using magnetic resonance imaging from 36 subjects. The performance of the method was evaluated using a liver segmentation task. The result was then compared to the corresponding performance of direct segmentation using Dice Index statistics. The method shows a significant improvement in liver segmentation performance between the proposed method and direct segmentation. Furthermore, the standard deviation in performance decreased significantly. Using competing complementary manifolds defined over a hierarchy of region of interests gives an additional improvement in segmentation performance compared to the single manifold segmentation.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 112, p. 340-345
Keywords [en]
Atlas-based segmentation, Image registration, Manifold learning, MRI
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-148304DOI: 10.1016/j.patrec.2018.04.037ISI: 000443950800049OAI: oai:DiVA.org:liu-148304DiVA, id: diva2:1214568
Available from: 2018-06-07 Created: 2018-06-07 Last updated: 2019-06-14Bibliographically approved

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Andersson, ThordBorga, MagnusDahlqvist Leinhard, Olof
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Department of Biomedical EngineeringFaculty of Science & EngineeringCenter for Medical Image Science and Visualization (CMIV)Division of Biomedical EngineeringDivision of Radiological SciencesFaculty of Medicine and Health SciencesDepartment of Radiation Physics
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Pattern Recognition Letters
Medical Image Processing

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