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Automated segmentation of midbrain structures with high iron content
Stockholm University, Faculty of Social Sciences, Aging Research Center (ARC), (together with KI).
Stockholm University, Faculty of Social Sciences, Aging Research Center (ARC), (together with KI).
Stockholm University, Faculty of Social Sciences, Aging Research Center (ARC), (together with KI).
Number of Authors: 42018 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 170, p. 199-209Article, review/survey (Refereed) Published
Abstract [en]

The substantia nigra (SN), the subthalamic nucleus (STN), and the red nucleus (RN) are midbrain structures of ample interest in many neuroimaging studies, which may benefit from the availability of automated segmentation methods. The high iron content of these structures awards them high contrast in quantitative susceptibility mapping (QSM) images. We present a novel segmentation method that leverages the information of these images to produce automated segmentations of the SN, STN, and RN. The algorithm builds a map of spatial priors for the structures by non-linearly registering a set of manually-traced training labels to the midbrain. The priors are used to inform a Gaussian mixture model of the image intensities, with smoothness constraints imposed to ensure anatomical plausibility. The method was validated on manual segmentations from a sample of 40 healthy younger and older subjects. Average Dice scores were 0.81 (0.05) for the SN, 0.66 (0.14) for the STN and 0.88 (0.04) for the RN in the left hemisphere, and similar values were obtained for the right hemisphere. In all structures, volumes of manual and automatically obtained segmentations were significantly correlated. The algorithm showed lower accuracy on R-2* and T-2-weighted Fluid Attenuated Inversion Recovery (FLAIR) images, which are also sensitive to iron content. To illustrate an application of the method, we show that the automated segmentations were comparable to the manual ones regarding detection of age-related differences to putative iron content.

Place, publisher, year, edition, pages
2018. Vol. 170, p. 199-209
Keywords [en]
Brain iron, Quantitative susceptibility mapping, R2*, Relaxometry, FLAIR, Segmentation
National Category
Basic Medicine Clinical Medicine Medical Engineering
Identifiers
URN: urn:nbn:se:su:diva-155898DOI: 10.1016/j.neuroimage.2017.06.016ISI: 000429940900016PubMedID: 28602813OAI: oai:DiVA.org:su-155898DiVA, id: diva2:1202922
Available from: 2018-05-02 Created: 2018-05-02 Last updated: 2018-05-02Bibliographically approved

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