Bayesian segmentation of brainstem structures in MRI

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Iglesias, Juan Eugenio Van Leemput, Koen Bhatt, Priyanka Casillas, Christen Dutt, Shubir Schuff, Norbert Truran-Sacrey, Diana Boxer, Adam L. Fischl, Bruce 2017-04-28T09:52:49Z 2017-04-28T09:52:49Z 2015
dc.identifier.citation Iglesias , J E , Van Leemput , K , Bhatt , P , Casillas , C , Dutt , S , Schuff , N , Truran-Sacrey , D , Boxer , A L & Fischl , B 2015 , ' Bayesian segmentation of brainstem structures in MRI ' NEUROIMAGE , vol 113 , pp. 184-195 . DOI: 10.1016/j.neuroimage.2015.02.065 en
dc.identifier.issn 1053-8119
dc.identifier.issn 1095-9572
dc.identifier.other PURE UUID: 1592e4cb-106b-4805-a558-e920f92a4893
dc.identifier.other PURE ITEMURL:
dc.identifier.other PURE FILEURL:
dc.description VK: Lampinen, J.
dc.description.abstract In this paper we present a method to segment four brainstem structures (midbrain, pons, medulla oblongata and superior cerebellar peduncle) from 3D brain MRI scans. The segmentation method relies on a probabilistic atlas of the brainstem and its neighboring brain structures. To build the atlas, we combined a dataset of 39 scans with already existing manual delineations of the whole brainstem and a dataset of 10 scans in which the brainstem structures were manually labeled with a protocol that was specifically designed for this study. The resulting atlas can be used in a Bayesian framework to segment the brainstem structures in novel scans. Thanks to the generative nature of the scheme, the segmentation method is robust to changes in MRI contrast or acquisition hardware. Using cross validation, we show that the algorithm can segment the structures in previously unseen T1 and FLAIR scans with great accuracy (mean error under 1 mm) and robustness (no failures in 383 scans including 168 AD cases). We also indirectly evaluate the algorithm with a experiment in which we study the atrophy of the brainstem in aging. The results show that, when used simultaneously, the volumes of the midbrain, pons and medulla are significantly more predictive of age than the volume of the entire brainstem, estimated as their sum. The results also demonstrate that the method can detect atrophy patterns in the brainstem structures that have been previously described in the literature. Finally, we demonstrate that the proposed algorithm is able to detect differential effects of AD on the brainstem structures. The method will be implemented as part of the popular neuroimaging package FreeSurfer. en
dc.format.extent 184-195
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries NEUROIMAGE en
dc.relation.ispartofseries Volume 113 en
dc.rights openAccess en
dc.title Bayesian segmentation of brainstem structures in MRI en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science en
dc.contributor.department Department of Neuroscience and Biomedical Engineering en
dc.subject.keyword Brainstem
dc.subject.keyword Bayesian segmentation
dc.subject.keyword Probabilistic atlas
dc.identifier.urn URN:NBN:fi:aalto-201704283704
dc.identifier.doi 10.1016/j.neuroimage.2015.02.065
dc.type.version publishedVersion

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