Region of Interest Identification for Brain Tumors in Magnetic Resonance Images

Mostafaie, Fateme; Teimouri, Reihaneh; Babak, Zahra Nabizadeh Shahre; Karimi, Nader; Samavi, Shadrokh

doi:10.1109/icee50131.2020.9260611

Cited by 4 publications

(1 citation statement)

References 12 publications

(11 reference statements)

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“…After the full-text screening, 223 studies are included for synthesis. Among them, 61 are physics or mathematics-based, 13–74 156 are deep learning-based and six are software-based or semi-automatic 75–80 methods articles.…”

Section: Resultsmentioning

confidence: 99%

Magnetic resonance image-based brain tumour segmentation methods: A systematic review

2022

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Background Image segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development. Purpose To determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation. Methods We conducted a systematic review of 572 brain tumour segmentation studies during 2015–2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score). Statistical tests We compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour. Results We found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation. Conclusion U-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available.

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Section: Resultsmentioning

confidence: 99%

Magnetic resonance image-based brain tumour segmentation methods: A systematic review

2022

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A Dual Cascaded Deep Theoretic Learning Approach for the Segmentation of the Brain Tumors in MRI Scans

Sreedhar,

Dara,

Srinivasulu

et al. 2024

Int J Imaging Syst Tech

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Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is crucial for diagnosis, treatment planning, and monitoring of patients with neurological disorders. This paper proposes an approach for brain tumor segmentation employing a cascaded architecture integrating L‐Net and W‐Net deep learning models. The proposed cascaded model leverages the strengths of U‐Net as a baseline model to enhance the precision and robustness of the segmentation process. In the proposed framework, the L‐Net excels in capturing the mask, while the W‐Net focuses on fine‐grained features and spatial information to discern complex tumor boundaries. The cascaded configuration allows for a seamless integration of these complementary models, enhancing the overall segmentation performance. To evaluate the proposed approach, extensive experiments were conducted on the datasets of BraTs and SMS Medical College comprising multi‐modal MRI images. The experimental results demonstrate that the cascaded L‐Net and W‐Net model consistently outperforms individual models and other state‐of‐the‐art segmentation methods. The performance metrics such as the Dice Similarity Coefficient value achieved indicate high segmentation accuracy, while Sensitivity and Specificity metrics showcase the model's ability to correctly identify tumor regions and exclude healthy tissues. Moreover, the low Hausdorff Distance values confirm the model's capability to accurately delineate tumor boundaries. In comparison with the existing methods, the proposed cascaded scheme leverages the strengths of each network, leading to superior performance compared to existing works of literature.

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Research on the Application of Artificial Intelligence in Brain Functional Imaging

Han

Qiao

2022

Lecture Notes in Electrical Engineering

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Region of Interest Identification for Brain Tumors in Magnetic Resonance Images

Cited by 4 publications

References 12 publications

Magnetic resonance image-based brain tumour segmentation methods: A systematic review

Magnetic resonance image-based brain tumour segmentation methods: A systematic review

A Dual Cascaded Deep Theoretic Learning Approach for the Segmentation of the Brain Tumors in MRI Scans

Research on the Application of Artificial Intelligence in Brain Functional Imaging

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