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BTS-ADCNN: brain tumor segmentation based on rapid anisotropic diffusion function combined with convolutional neural network using MR images

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Abstract

Brain cancer is a fatal and debilitating condition that has a profoundly negative impact on patients' lives. Therefore, early diagnosis of brain tumors enhances the effectiveness of treatment and raises patient survival rates. However, it is a challenging task and an unmet need to identify brain tumors in their early stages. In the presented work, a rapid and efficient algorithm for tumor segmentation that supports doctors in the practice of screening brain tumors is proposed. The proposed method is divided into two phases: Firstly, a preprocessing operation is performed using an anisotropic diffusion filtering function for noise removal with details and edge conservation. The second phase is a segmentation operation of brain tumors based on deep convolutional neural network. Simulation results on reel data approve the efficiency of the proposed method. In fact, the combined filtering and segmentation methods have improved the segmentation results of Dice similarity coefficient (Dice = 89.65 ± 0.81%), Hausdorff distance (95%) (HD95) = 7.53, and Intersection over Union value (IOU = 90.12 ± 0.76%) using a set of 520 MR images divided into: 364 images for the training and 156 images for the validation. Compared to different recent segmentation methods, the proposed technique offers an advanced performance by detecting Glioblastoma tumor regions. The obtained results are very interesting and prove the efficiency of the proposed algorithm compared to other recent works in the literature.

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Data availability

For data evaluation, please contact Dr. Ben Slama Amine at amine.benslama@istmt.utm.tn. All data analyzed during this study are included in this published article: Henry, T., Carré, A., Lerousseau, M., Estienne, T., Robert, C., Paragios, N., and Deutsch, E. (2021). Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-Net neural networks: a BraTS 2020 challenge solution. In Brainlesion: Glioma, Multiple Sclerosis, Stroke, and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I 6 (pp. 327–339). Springer International Publishing.

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Acknowledgments

We are deeply grateful to all those who contributed to the success of this research project.

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Authors and Affiliations

  1. RIFTSI Research Laboratory, ENSIT, University of Tunis, 1008, Tunis, Tunisia

    Zouhair Mbarki & Hassene Seddik

  2. Laboratory of Biophysics and Medical Technologies, LR13ES07 (BTM), Higher Institute of Medical Technologies of Tunis (ISTMT), University of Tunis EL Manar, 1006, Tunis, Tunisia

    Amine Ben Slama & Hedi Trabelsi

  3. Biochemistry Laboratory, Bechir Hamza Children’s Hospital, Bab Saadoun Square, 1007, Tunis, Tunisia

    Yessine Amri

  4. Department of Educational Sciences, Higher Institute of Applied Studies in Humanity Le Kef, University of Jendouba, Le Kef, Tunisia

    Yessine Amri

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  1. Zouhair Mbarki
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  4. Hedi Trabelsi
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  5. Hassene Seddik
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Contributions

All Authors have contributed to the drafting and the critical revision of the article. The final version was approved by all authors.

Corresponding author

Correspondence to Zouhair Mbarki.

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The authors declare no competing interests.

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This study was carried out under the principles of the Declaration of Helsinki developed by the World Medical Association and approved by the Human Ethics committee of Bechir Hamza Children’s Hospital of Tunis.

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The informed consent was waived by the Human Ethics committee of Bechir Hamza Children’s Hospital of Tunis.

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Mbarki, Z., Ben Slama, A., Amri, Y. et al. BTS-ADCNN: brain tumor segmentation based on rapid anisotropic diffusion function combined with convolutional neural network using MR images. J Supercomput 80, 13272–13294 (2024). https://doi.org/10.1007/s11227-024-05985-2

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