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Brain hyperintensities: automatic segmentation of white matter hyperintensities in clinical brain MRI images using improved deep neural network

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Abstract

White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. Automatic WMH segmentation is essential for evaluating the natural disease course and the efficacy of clinical interventions, especially drug discovery. This work presents a hybrid method, which combines the novel metaheuristic Slime Mold Algorithm with the Harris Hawk's Optimization algorithm to train the Deep Convolutional Neural Network (DCNN) as a robust model to optimize the Kapur’s entropy for WMH segmentation. We named it as Slime Mold Harris Hawk's Optimization-based DCNN (SMHHO-DCNN). The proposed method was tested on the open WMH segmentation challenge MICCAI 2017 and in-house dataset. However, the WMH regions would appear blurry and globally inconsistent. In order to solve the fuzzy problem, we improved the network architecture by incorporating skip connections across the mirrored layers with in encoder and decoder stacks. As a result, the segmented WMH output appears more realistic and coherent with its surrounding contexts, which enables the hybrid attention module to further enhance the precision of WMH localization by extracting the supporting information of high-level characteristics and low-level features. As a result, that the proposed hybrid optimization, skip connections, has increased the segmentation performance of Dice score to 87.2%, precision 90.3%, recall 89.5%, and f1-score 88.8% for MRI dataset which is superior compared to other approaches. The proposed technique outperformed existing state-of-the-art methodologies in both qualitative and quantitative measurements across a wide range of medical modalities. Furthermore, the dataset contains T1-w, T2-w, and FLAIR images, demonstrating the adaptability and robustness of the model.

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Data availability

The datasets analyzed during the current study are not publicly available due to individual privacy but are available from the corresponding author on reasonable request.

Notes

  1. https://wmh.isi.uu.nl/data/, https://doi.org/10.1002/hbm.25695, https://grand-challenge.org/challenges/.

  2. https://doi.org/10.3389/fnagi.2022.915009.

  3. https://doi.org/10.1109/TMI.2019.2905770.

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Acknowledgments

We would like to express our sincere appreciation and gratitude to Dr. Surendra Nath Senapati, Department of Radiation Oncology, SCB Medical College & Hospital, Cuttack, India, for his invaluable contribution to the clinical validation of our research work.

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No funding was received for conducting this study.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Faculty of Science and Technology (IcfaiTech), ICFAI Foundation for Higher Education, Hyderabad, India

    Puranam Revanth Kumar, Rajesh Kumar Jha & P. Akhendra Kumar

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Contributions

Puranam Revanth Kumar helped in conceptualization, methodology, software, data collection, implementation, and writing—original draft preparation. Rajesh Kumar Jha helped in data collection, visualization, writing—review, and supervision. P Akhendra Kumar helped in visualization, writing—review, and supervision (support).

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Correspondence to Puranam Revanth Kumar or Rajesh Kumar Jha.

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Kumar, P.R., Jha, R.K. & Kumar, P.A. Brain hyperintensities: automatic segmentation of white matter hyperintensities in clinical brain MRI images using improved deep neural network. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06080-2

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