Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Hameurlaine, Messaoud; Moussaoui, Abdelouahab

doi:10.5101/nbe.v11i2.p178-191

Cited by 22 publications

(11 citation statements)

References 51 publications

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“…The authors in [11,44,93] covered a majority of solutions from BraTS2012 to BraTS2018 challenges, lacking, however, an analyses based on methodology category and highlights. Two recent surveys by Kapoor et al [71] and Hameurlaine et al [51] also focused on the overview of classic brain tumor segmentation methods. However, both of them lacked the technical analysis and discussion of deep learning based segmentation methods.…”

Section: Difference From Previous Surveysmentioning

confidence: 99%

Deep learning based brain tumor segmentation: a survey

Liu

Tong

Chen

et al. 2022

Complex Intell. Syst.

171

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Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we provide this survey with a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 150 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions.

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Section: Difference From Previous Surveysmentioning

confidence: 99%

Deep learning based brain tumor segmentation: a survey

Liu

Tong

Chen

et al. 2022

Complex Intell. Syst.

171

View full text Add to dashboard Cite

show abstract

“…It was also the focus of two recent studies, one in [ 10 ] and the other in [ 11 ], which summarized standard brain tumor segmentation approaches. Both of them lacked a technical analysis and description of deep learning-based segmentation methods, which were necessary for their respective fields.…”

Section: Related Workmentioning

confidence: 99%

Region Convolutional Neural Network for Brain Tumor Segmentation

Pitchai¹,

Praveena²,

Murugeswari

et al. 2022

Computational Intelligence and Neuroscience

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Gliomas are often difficult to find and distinguish using typical manual segmentation approaches because of their vast range of changes in size, shape, and appearance. Furthermore, the manual annotation of cancer tissue segmentation under the close supervision of a human professional is both time-consuming and exhausting to perform. It will be easier and faster in the future to get accurate and quick diagnoses and treatments thanks to automated segmentation and survival rate prediction models that can be used now. In this article, a segmentation model is designed using RCNN that enables automatic prognosis on brain tumors using MRI. The study adopts a U-Net encoder for capturing the features during the training of the model. The feature extraction extracts geometric features for the estimation of tumor size. It is seen that the shape, location, and size of a tumor are significant factors in the estimation of prognosis. The experimental methods are conducted to test the efficacy of the model, and the results of the simulation show that the proposed method achieves a reduced error rate with increased accuracy than other methods.

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“…Several machine learning approaches have been proposed and can be classified into two main categories: generative models and discriminative models. The latter have emerged from democratization of deep learning and convolutional neuronal networks (CNN) [42]. Unlike traditional classification methods, where images with manually tagged features are used for training, CNNs automatically learn complex representative features directly from the data itself.…”

Section: Tumor Segmentationmentioning

confidence: 99%