Automatic Brain Tumor Segmentation in Multispectral MRI Volumes Using a Random Forest Approach

Kapás, Zoltán; Lefkovits, László; Iclănzan, David; Győrfi, Ágnes; Iantovics, László Barna; Lefkovits, Szidónia; Szilágyi, Sándor; Szilágyi, László

doi:10.1007/978-3-319-75786-5_12

Cited by 6 publications

(4 citation statements)

References 23 publications

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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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“…For efficient segmentation of the brain tumour region, random forest (RF) and binary decision tree (BBD) use multi-spectral MR images. By reducing the effect of relative intensities and increasing the features information at each voxel of the MR image, random forest and bagged decision tree (RF-BDT) preprocesses the image dataset [8]. D. presented semi-automatic images segmentation (SAMBAS).…”

Section: Literature Survey 21 Exisitng Methodologiesmentioning

confidence: 99%

Cancerous brain tumor detection using hybrid deep learning framework

Tidke¹,

Chiwhane²,

Jain

et al. 2022

IJEECS

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Computational <span>models based on deep learning (DL) algorithms have multiple processing layers representing data at multiple levels of abstraction. Deep learning has exploded in popularity in recent years, particularly in medical image processing, medical image analysis, and bioinformatics. As a result, deep learning has effectively modified and strengthened the means of identification, prediction, and diagnosis in several healthcare fields, including pathology, brain tumours, lung cancer, the abdomen, cardiac, and retina. In general, brain tumours are among the most common and aggressive malignant tumour diseases, with a limited life span if diagnosed at a higher grade. After identifying the tumour, brain tumour grading is a crucial step in evaluating a successful treatment strategy. This research aims to propose a cancerous brain tumor detection and classification using deep learning. In this paper, numerous soft computing techniques and a deep learning model to summarise the pathophysiology of brain cancer, imaging modalities for brain cancer, and automated computer-assisted methods for brain cancer characterization is used. In the sense of machine learning and the deep learning model, paper has highlighted the association between brain cancer and other brain disorders such as epilepsy, stroke, Alzheimer's, Parkinson's, and Wilson's disease, leukoaraiosis, and other neurological disorders.</span>

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“…Random Forest (RF) and Binary Decision Tree use multi-spectral MR images for efficient segmentation of the brain tumor region. RF-BDT preprocess the image dataset by reducing the effect of relative intensities and increase the features information at each voxel of the MR image [120].…”

Section: Approaches Toward Automatic Segmentationmentioning

confidence: 99%

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

et al. 2020

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Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.

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Automatic Brain Tumor Segmentation in Multispectral MRI Volumes Using a Random Forest Approach

Cited by 6 publications

References 23 publications

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

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

Cancerous brain tumor detection using hybrid deep learning framework

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

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