Survey on brain tumor segmentation and feature extraction of MR images

Saman, Sangeetha; Narayanan, Swathi Jamjala

doi:10.1007/s13735-018-0162-2

Cited by 60 publications

(25 citation statements)

References 116 publications

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“…There are many methods reported in literature on brain tumor segmentation that include intensity-based, atlasbased, deformable model-base, hybrid-based, and deep learning-based methods 29 . Recently, deep learning-based methods offered better performance for tumor segmentation 25,30,31 .…”

Section: Methodsmentioning

confidence: 99%

Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images

Pei

Vidyaratne

Rahman

et al. 2020

Sci Rep

101

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A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction using a hybrid method of deep learning and machine learning. To evaluate the performance, we apply the proposed methods to the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) dataset for tumor segmentation and overall survival prediction, and to the dataset of the Computational Precision Medicine Radiology-Pathology (CPM-RadPath) Challenge on Brain Tumor Classification 2019 for tumor classification. We also perform an extensive performance evaluation based on popular evaluation metrics, such as Dice score coefficient, Hausdorff distance at percentile 95 (HD95), classification accuracy, and mean square error. The results suggest that the proposed method offers robust tumor segmentation and survival prediction, respectively. Furthermore, the tumor classification results in this work is ranked at second place in the testing phase of the 2019 CPM-RadPath global challenge.

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Section: Methodsmentioning

confidence: 99%

Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images

Pei

Vidyaratne

Rahman

et al. 2020

Sci Rep

101

View full text Add to dashboard Cite

show abstract

“…The MRI segmentation is followed by feature extraction. The texture features, intensity features, shape-based features are extracted from each subregion [43]. Mean, variance, skewness related to pixel intensities can also be extracted for a particular tumor sub-region.…”

Section: ) Feature Extractionmentioning

confidence: 99%

Glioma Survival Analysis Empowered With Data Engineering—A Survey

et al. 2021

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Survival analysis is a critical task in glioma patient management due to the inter and intra tumor heterogeneity. In clinical practice, clinicians estimate the survival with their experience, which can be biased and optimistic. Over the past decades, diverse survival analysis approaches were proposed incorporating distinct data such as imaging and genetic information. The remarkable advancements in imaging and high throughput omics and sequencing technologies have enabled the acquisition of this information of glioma patients efficiently, providing novel insights for survival estimation in the present day. Besides, in the past years, machine learning techniques and deep learning have emerged into the field of survival analysis of glioma patients trading off the traditional statistical analysis-based survival analysis approaches. In this survey paper, we explore the prognostic parameters acquired, utilizing diagnostic imaging techniques and genomic platforms for survival or risk estimation of glioma patients. Further, we review the techniques, learning and statistical analysis algorithms, along with their benefits and limitations used for prognosis prediction. Consequently, we highlight the challenges of the existing state-of-the-art survival prediction studies and propose future directions in the field of research.

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“…Various authors have generally classified segmentation methods into three significant categories: Threshold-based approaches, region-based approaches, and pixel classification approaches [20,21,22]. Figure 3 reviews the various MRI segmentation approaches [23].…”

Section: Segmentation Techniquesmentioning

confidence: 99%

“…To assess cancer's detrimental impact, the instrument for brain tumor segmentation may be either automatic or semi-automatic, also serving as a necessary stage, enabling doctors to recognize the brain tumor before considering brain surgery. In the last years, numerous methods based on the tissue characteristic have been improved for MRI segmentation [9].…”

Section: Introductionmentioning

confidence: 99%

Automatic MRI Brain Tumor Segmentation Techniques: A Survey

Basir

Shantta

2021

IRA-JAS

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Image segmentation plays a crucial role in recognizing image signification for checking and mining medical image records. Brain tumor segmentation is a complicated assignment in medical image analysis. It is challenging to identify precisely and extract that a portion of the image has abnormal tissues for further diagnosis and analysis. The method of segmenting a tumor from a brain MRI image is a highly concentrated medical science community field, as MRI is non-invasive. In this survey, brain MRI images' latest brain tumor segmentation techniques are addressed a thoroughgoing literature review. Besides, surveys the several approved techniques regularly applied for brain tumor MRI segmentation. Also, highlighting variances among them and reviews their abilities, pros, and weaknesses. Various approaches to image segmentation are described and explicated with the modern participation of several investigators.

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Survey on brain tumor segmentation and feature extraction of MR images

Cited by 60 publications

References 116 publications

Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images

Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images

Glioma Survival Analysis Empowered With Data Engineering—A Survey

Automatic MRI Brain Tumor Segmentation Techniques: A Survey

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