Supervised Brain Tumor Segmentation Based on Gradient and Context-Sensitive Features

Zhao, Junting; Meng, Zhaopeng; Wei, Leyi; Sun, Changming; Zou, Quan; Su, Ran

doi:10.3389/fnins.2019.00144

Cited by 36 publications

(12 citation statements)

References 39 publications

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“…In order to successfully build well-generalizing deep models, we need huge amount of ground-truth data to avoid overfitting of such large-capacity learners, and "memorizing" training sets (LeCun et al, 2016). It has become a significant obstacle which makes deep neural networks quite challenging to apply in the medical image analysis field where acquiring high-quality groundtruth data is time-consuming, expensive, and very human-dependent, especially in the context of brain-tumor delineation from magnetic resonance imaging (MRI) (Isin et al, 2016;Angulakshmi and Lakshmi Priya, 2017;Marcinkiewicz et al, 2018;Zhao et al, 2019). Additionally, the majority of manually-annotated image sets are imbalanced-examples belonging to some specific classes are often under-represented.…”

Section: Introductionmentioning

confidence: 99%

Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

261

127

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Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal role in scenarios in which the amount of high-quality ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a very common problem in medical image analysis, especially tumor delineation. In this paper, we review the current advances in data-augmentation techniques applied to magnetic resonance images of brain tumors. To better understand the practical aspects of such algorithms, we investigate the papers submitted to the Multimodal Brain Tumor Segmentation Challenge (BraTS 2018 edition), as the BraTS dataset became a standard benchmark for validating existent and emerging brain-tumor detection and segmentation techniques. We verify which data augmentation approaches were exploited and what was their impact on the abilities of underlying supervised learners. Finally, we highlight the most promising research directions to follow in order to synthesize high-quality artificial brain-tumor examples which can boost the generalization abilities of deep models.

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Section: Introductionmentioning

confidence: 99%

Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

261

127

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“…Here, various segmentation approaches, such as supervised brain tumour segmentation [44], anatomy‐guided brain tumour segmentation [45], grey wolf optimisation (GWO)‐based multilevel thresholding [46], genetic algorithm (GA) [47], active contour, watershed transform, rapid segmentation using particle swarm optimisation (PSO), and deep joint segmentation are analysed based on the dice coefficient and the mean absolute distance and the results are plotted. Fig.…”

Section: Resultsmentioning

confidence: 99%

DeepJoint segmentation for the classification of severity‐levels of glioma tumour using multimodal MRI images

J²

2020

IET image process

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Brain tumour segmentation is the process of separating the tumour from normal brain tissues. A glioma is a kind of tumour, which fires up in the glial cells of the spine or the brain. This study introduces a technique for classifying the severity levels of glioma tumour using a novel segmentation algorithm, named DeepJoint segmentation and the multi‐classifier. Initially, the brain images are subjected to pre‐processing and the region of interest is extracted. Then, the segmentation of the pre‐processed image is done using the proposed DeepJoint segmentation, which is developed through the iterative procedure of joining the grid segments. After the segmentation, feature extraction is carried out from core and oedema tumours using information‐theoretic measures. Finally, the classification is done by the deep convolutional neural network (DCNN), which is trained by an optimisation algorithm, named fractional Jaya whale optimiser (FJWO). FJWO is developed by integrating the whale optimisation algorithm in fractional Jaya optimiser. The performance of the proposed FJWO–DCNN with the DeepJoint segmentation method is analysed using accuracy, true positive rate, specificity, and sensitivity. The results depicted that the proposed method produces a maximum accuracy of 96%, which indicates its superiority.

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“…Among various primary tumors, Gliomas is the most prominent tumor that possesses the highest mortality rate. An enhanced brain tumor segmentation technique was proposed to detect diverse tumor cells for both high-grade and low-grade gliomas in MRI images based on the gradient and the context-sensitive attributes [25]. The study also incorporates two and threedimensional gradient data for analyzing the gradient change.…”

Section: Literature Reviewmentioning

confidence: 99%

Dense Hierarchical CNN – A Unified Approach for Brain Tumor Segmentation

Sille¹,

Choudhury²,

Chauhan³

et al. 2021

RIA

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Brain tumor segmentation is an essential and challenging task because of the heterogeneous nature of neoplastic tissue in spatial and imaging techniques. Manual segmentation of the tumor in MRI images is prone to error and time-consuming tasks. An efficient segmentation mechanism is vital to the accurate classification and segmentation of tumorous cells. This study presents an efficient hierarchical clustering-based dense CNN approach for accurately classifying and segmenting the brain tumor cells in MRI images. The research focuses on improving the efficiency of the segmentation algorithms by considering the qualitative measures such as the dice score coefficient using quantitative parameters such as mean square error and peak signal to noise ratio. The experimental analysis states the efficacy and prominence of the proposed technique compared to other models are tabulated within the paper.

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Supervised Brain Tumor Segmentation Based on Gradient and Context-Sensitive Features

Cited by 36 publications

References 39 publications

Data Augmentation for Brain-Tumor Segmentation: A Review

Data Augmentation for Brain-Tumor Segmentation: A Review

DeepJoint segmentation for the classification of severity‐levels of glioma tumour using multimodal MRI images

Dense Hierarchical CNN – A Unified Approach for Brain Tumor Segmentation

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