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Segmentation of MRI brain scans using spatial constraints and 3D features

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Abstract

This paper presents a novel unsupervised algorithm for brain tissue segmentation in magnetic resonance imaging (MRI). The proposed algorithm, named Gardens2, adopts a clustering approach to segment voxels of a given MRI into three classes: cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM). Using an overlapping criterion, 3D feature descriptors and prior atlas information, Gardens2 generates a segmentation mask per class in order to parcellate the brain tissues. We assessed our method using three neuroimaging datasets: BrainWeb, IBSR18, and IBSR20, the last two provided by the Internet Brain Segmentation Repository. Its performance was compared with eleven well established as well as newly proposed unsupervised segmentation methods. Overall, Gardens2 obtained better segmentation performance than the rest of the methods in two of the three databases and competitive results when its performance was measured by class.

Brain tissue segmentation using 3D features and an adjusted atlas template

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Acknowledgments

We thank P. Reynoso-Arenas, Department of Pediatric Hematology, National Medical Center La Raza. IMSS, Mexico City, Mexico, for helping us with the clinical interpretation of the results in this study.

Funding

This work was partially supported by the National Council of Science and Technology in Mexico (CONACYT) through the scholarship #553739 provided to J. Grande-Barreto.

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  1. National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico

    Jonas Grande-Barreto & Pilar Gómez-Gil

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  1. Jonas Grande-Barreto
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  2. Pilar Gómez-Gil
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Correspondence to Jonas Grande-Barreto.

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Grande-Barreto, J., Gómez-Gil, P. Segmentation of MRI brain scans using spatial constraints and 3D features. Med Biol Eng Comput 58, 3101–3112 (2020). https://doi.org/10.1007/s11517-020-02270-1

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  • DOI: https://doi.org/10.1007/s11517-020-02270-1

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