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Deep pattern-based tumor segmentation in brain MRIs

  • S.I. : Emerging trends in AI & ML
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Abstract

It is still hard to deal with artifacts in magnetic resonance images (MRIs), particularly when the latter are to be segmented. This paper introduces a novel deep-based scheme for tumor segmentation in brain MRIs. According to the proposed scheme, a large set of partial sub-images are paved form sliced from an MRI volume then inputted to an ensemble of convolutional neural networks (CNNs) in order to label the voxels in the centers of the sub-images, according to the classes to which they should belong. Partial sub-images, that capture local patterns around central voxels, have allowed to speed-up both the training and the prediction steps, allowing efficient use of such a scheme for real MRI-based tumor diagnosis. Experiments were performed using the BraTS (brain tumor segmentation) database, where the obtained results show that the proposed scheme allows both fast and accurate brain tumor detection and segmentation in pathological MRIs.

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  1. LICUS Lab., University 20 aout 1955, BP 26 Route El-Hadaik, 21000, Skikda, Algeria

    Nadjet Bouchaour & Smaine Mazouzi

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  1. Nadjet Bouchaour
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  2. Smaine Mazouzi
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Correspondence to Nadjet Bouchaour.

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Bouchaour, N., Mazouzi, S. Deep pattern-based tumor segmentation in brain MRIs. Neural Comput & Applic 34, 14317–14326 (2022). https://doi.org/10.1007/s00521-022-07422-y

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