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3DCC-MPNN: automated 3D reconstruction of corpus callosum based on modified PNN and marching cubes

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Abstract

The present study addresses the segmentation and the 3D reconstruction of the corpus callosum from MRI scans. Accurate segmentation of the corpus callosum is essential in order to enable its reconstruction and 3D visualization to facilitate early diagnosis. In fact, many studies have established a strong correlation between the shape of the corpus callosum and several pathological conditions. However, the segmentation is made difficult by regions of similar intensity within the MRI images. To overcome this challenge, we propose an automated method that relies mainly on a probabilistic neural network applied to superpixels. The proposed scheme involves segmenting the corpus callosum within the MRI scans, followed by the application of the marching cubes technique in order to generate 3D volumes. The effectiveness of the proposed method has been extensively validated on four challenging datasets (OASIS, ABIDE, MIRIAD, and SBD), and the obtained results demonstrate its superior performance compared to other state-of-the-art methods.

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Availability of data and materials

The four datasets OASIS, ABIDE, MIRIAD, and SBD generated and analyzed during the current study are available respectively in https://www.oasis-brains.org/, https://www.datacatalog.med.nyu.edu/dataset/10452, https://www.nitrc.org/projects/miriad/ and https://www.brainweb.bic.mni.mcgill.ca/.

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Authors and Affiliations

  1. Institut Supérieur d’Informatique, Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de Recherche en Informatique, Modélisation et Traitement de l’Information et de la Connaissance (LIMTIC), Université de Tunis El manar, 2 Rue Abou Rayhane Bayrouni, 2080, Ariana, Tunisia

    Amal Jlassi, Khaoula Elbedoui & Walid Barhoumi

  2. Université de Carthage, Ecole Nationale d’Ingénieurs de Carthage, 45 Rue des Entrepreneurs, 2035, Tunis-Carthage, Tunisia

    Khaoula Elbedoui & Walid Barhoumi

  3. Nuclear Medicine Department, Pasteur Institute of Tunis, Tunis, Tunisia

    Chokri Maktouf

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  1. Amal Jlassi
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  2. Khaoula Elbedoui
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  3. Walid Barhoumi
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  4. Chokri Maktouf
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Jlassi, A., Elbedoui, K., Barhoumi, W. et al. 3DCC-MPNN: automated 3D reconstruction of corpus callosum based on modified PNN and marching cubes. Evolving Systems 15, 1817–1843 (2024). https://doi.org/10.1007/s12530-024-09591-8

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