Bayesian segmentation of brainstem structures in MRI

Juan Eugenio Iglesias, Koen Van Leemput, Priyanka Bhatt, Christen Casillas, Shubir Dutt, Norbert Schuff, Diana Truran-Sacrey, Adam Boxer, Bruce Fischl

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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 1mm) 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.
Original languageEnglish
JournalNeuroImage
Volume113
Pages (from-to)184-195
ISSN1053-8119
DOIs
Publication statusPublished - 2015

Keywords

  • Brainstem
  • Bayesian segmentation
  • Probabilistic atlas

Cite this

Iglesias, J. E., Van Leemput, K., Bhatt, P., Casillas, C., Dutt, S., Schuff, N., ... Fischl, B. (2015). Bayesian segmentation of brainstem structures in MRI. NeuroImage, 113, 184-195. https://doi.org/10.1016/j.neuroimage.2015.02.065
Iglesias, Juan Eugenio ; Van Leemput, Koen ; Bhatt, Priyanka ; Casillas, Christen ; Dutt, Shubir ; Schuff, Norbert ; Truran-Sacrey, Diana ; Boxer, Adam ; Fischl, Bruce. / Bayesian segmentation of brainstem structures in MRI. In: NeuroImage. 2015 ; Vol. 113. pp. 184-195.
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Iglesias, JE, Van Leemput, K, Bhatt, P, Casillas, C, Dutt, S, Schuff, N, Truran-Sacrey, D, Boxer, A & Fischl, B 2015, 'Bayesian segmentation of brainstem structures in MRI', NeuroImage, vol. 113, pp. 184-195. https://doi.org/10.1016/j.neuroimage.2015.02.065

Bayesian segmentation of brainstem structures in MRI. / Iglesias, Juan Eugenio; Van Leemput, Koen; Bhatt, Priyanka; Casillas, Christen; Dutt, Shubir; Schuff, Norbert; Truran-Sacrey, Diana; Boxer, Adam; Fischl, Bruce.

In: NeuroImage, Vol. 113, 2015, p. 184-195.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Bayesian segmentation of brainstem structures in MRI

AU - Iglesias, Juan Eugenio

AU - Van Leemput, Koen

AU - Bhatt, Priyanka

AU - Casillas, Christen

AU - Dutt, Shubir

AU - Schuff, Norbert

AU - Truran-Sacrey, Diana

AU - Boxer, Adam

AU - Fischl, Bruce

PY - 2015

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N2 - 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 1mm) 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.

AB - 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 1mm) 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.

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KW - Bayesian segmentation

KW - Probabilistic atlas

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DO - 10.1016/j.neuroimage.2015.02.065

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VL - 113

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JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

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