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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Data Augmentation for Brain-Tumor Segmentation: A Review.

One of the most crucial tasks in any brain tumor detection system is the isolation of abnormal tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has effectively utilized the concepts of medical image processing, particularly on MR images, to automate the core steps, i.e. extraction, segmentation, classification for proximate detection of tumor. Research is more inclined towards MR for its non-invasive imaging properties. Computer aided diagnosis or detection systems are becoming challenging and are still an open problem due to variability in shapes, areas, and sizes of tumor. The past works of many researchers under medical image processing and soft computing have made noteworthy review analysis on automatic brain tumor detection techniques focusing segmentation as well as classification and their combinations. In the manuscript, various brain tumor detection techniques for MR images are reviewed along with the strengths and difficulties encountered in each to detect various brain tumor types. The current segmentation, classification and detection techniques are also conferred emphasizing on the pros and cons of the medical imaging approaches in each modality. The survey presented here aims to help the researchers to derive the essential characteristics of brain tumor types and identifies various segmentation/classification techniques which are successful for detection of a range of brain diseases. The manuscript covers most relevant strategies, methods, their working rules, preferences, constraints, and their future snags on MR image brain tumor detection. An attempt to summarize the current state-of-art with respect to different tumor types would help researchers in exploring future directions.

A survey on brain tumor detection techniques for MR images

The introduction of quantitative image analysis has given rise to fields such as radiomics which have been used to predict clinical sequelae. One growing area of interest for analysis is brain tumours, in particular glioblastoma multiforme (GBM). Tumour segmentation is an important step in the pipeline in the analysis of this pathology. Manual segmentation is often inconsistent as it varies between observers. Automated segmentation has been proposed to combat this issue. Methodologies such as convolutional neural networks (CNNs) which are machine learning pipelines modelled on the biological process of neurons (called nodes) and synapses (connections) have been of interest in the literature. We investigate the role of CNNs to segment brain tumours by firstly taking an educational look at CNNs and perform a literature search to determine an example pipeline for segmentation. We then investigate the future use of CNNs by exploring a novel field—radiomics. This examines quantitative features of brain tumours such as shape, texture, and signal intensity to predict clinical outcomes such as survival and response to therapy.

/pdf/convolutional-neural-networks-for-brain-tumour-segmentation-1hacgx4ut9.pdf

Convolutional neural networks for brain tumour segmentation

This study presents a new fully automated, fast, and accurate brain tumour segmentation method which automatically detects and extracts whole tumours from 3D-MRI. The proposed method is based on a hybrid approach that relies on a brain symmetry analysis method and a combining region-based and boundary-based segmentation methods. The segmentation process consists of three main stages. In the first one, image pre-processing is applied to remove any noise, and to extract the brain from the head image. In the second stage, automated tumour detection is performed. It is based essentially on FBB method using brain symmetry. The obtained result constitutes the automatic initialisation of a deformable model, thus removing the need of selecting the initial region of interest by the user. Finally, the third stage focuses on the application of region growing combined with 3D deformable model based on geodesic level-set to detect the tumour boundaries containing the initial region, computed previously, regardless of its shape and size. The proposed segmentation system has been tested and evaluated on 3D-MRIs of 285 subjects with different tumour types and shapes obtained from BraTS'2017 dataset. The obtained results turn out to be promising and objective as well as close to ground truth data.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2017.1124

Fully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level sets

Longitudinal brain tumor segmentation prediction in MRI using feature and label fusion.

Automatic segmentation of brain tumour is the process of separating abnormal tissues from normal tissues, such as white matter WM, gray matter GM, and cerebrospinal fluid CSF. The process of segmentation is still challenging due to the diversity of shape, location, and size of the tumour segmentation. The metabolic process, psychological process, and detailed information of the images, are obtained using positron emission tomography PET image, Computer Tomography CT image and Magnetic Resonance Image MRI. Multimodal imaging techniques such as PET/CT and PET/MRI that combine the information from many imaging techniques contribute more for accurate brain tumour segmentation. In this article, a comprehensive overview of recent automatic brain tumour segmentation techniques of MRI, PET, CT, and multimodal imaging techniques has been provided. The methods, techniques, their working principle, advantages, their limitations, and their future challenges are discussed in this article. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 66-77, 2017

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ima.22211

Automated brain tumour segmentation techniques- A review

The automated brain tumor segmentation methods are challenging due to the diverse nature of tumors. Recently, the graph based spectral clustering method is utilized for brain tumor segmentation to make high-quality segmentation output. In this paper, a new Walsh Hadamard Transform (WHT) texture for superpixel based spectral clustering is proposed for segmentation of a brain tumor from multimodal MRI images. First, the selected kernels of WHT are utilized for creating texture saliency maps and it becomes the input for the Simple Linear Iterative Clustering (SLIC) algorithm, to generate more precise texture based superpixels. Then the texture superpixels become nodes in the graph of spectral clustering for segmenting brain tumors of MRI images. Finally, the original members of superpixels are recovered to represent Complete Tumor (CT), Tumor Core (TC) and Enhancing Tumor (ET) tissues. The observational results are taken out on BRATS 2015 datasets and evaluated using the Dice Score (DS), Hausdorff Distance (HD) and Volumetric Difference (VD) metrics. The proposed method produces competitive results than other existing clustering methods.

Walsh Hadamard Transform for Simple Linear Iterative Clustering (SLIC) Superpixel Based Spectral Clustering of Multimodal MRI Brain Tumor Segmentation

BACKGROUND The automatic segmentation of brain tumour from MRI medical images is mainly covered in this review. Recently, state-of-the-art performance is provided by deep learning- based approaches in the field of image classification, segmentation, object detection, and tracking tasks. INTRODUCTION The core feature deep learning approach is the hierarchical representation of features from images, thus avoiding domain-specific handcrafted features. METHODS In this review paper, we have dealt with a review of Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation. First, we have discussed the basic architecture and approaches for deep learning methods. Secondly, we have discussed the literature survey of MRI brain tumour segmentation using deep learning methods and its multimodality fusion. Then, the advantages and disadvantages of each method are analyzed and finally, it is concluded with a discussion on the merits and challenges of deep learning techniques. RESULTS The review of brain tumour identification using deep learning. CONCLUSION Techniques may help the researchers to have a better focus on it.

A Review on Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation.

The automated brain tumor segmentation methods are challenging due to the diverse nature of tumors. Recently, the graph based Spectral Clustering (SC) method is utilized for brain tumor segmentation to create high‐quality clusters. In this article, a new superpixel based SC using the Walsh Hadamard texture feature for multimodal brain tumor segmentation from Magnetic Resonance Image is proposed. The selected kernels of Walsh Hadamard Transform (WHT) are projected on equal size blocks of the image for texture feature extraction. The texture feature strength of each block is considered as superpixels, and these superpixels become nodes in the graph of SC. Finally, the original members of superpixels are recovered to represent Complete Tumor (CT), Tumor Core (TC), and Enhancing Tumor (ET) tissues. The observational results are brought out on BRATS 2015 data sets and evaluated using the Dice Score (DS), Hausdorff Distance, and Volumetric Difference metrics. The proposed method has produced competitive results with a DS of 0.83 for CT, 0.75 for TC, and 0.73 for ET, respectively, for high‐grade images. In case of low‐grade images, the proposed method achieves DS of 0.78 for CT, 0.68 for TC, and 0.60 for ET, respectively. The proposed method produces results better than other existing clustering methods.

Walsh Hadamard kernel‐based texture feature for multimodal MRI brain tumour segmentation

The categorical relationships play a crucial role for choosing of computer database. Based on it, association rules display the link among the 2 categories. Because the object oriented elegance for the computer code cultivates, the relationships among the categories. Additionally rise near the complicated computer code style. However, one will develop improved object-oriented style through right association rules among groups or categories. The present paper deals to style the proper association rules for the object-oriented databases taken from the categories. A true scenario of Electricity Bill Deposit System (EBDS) and user interested items by analyzing user behavior history in social network environment. Both applications are taken into account in Indian situation and Apriori Algorithmic rule is employed for locating the employment of recurrent information sets through the proper association rules. The association rules area unit deliberate through famous Unified Modeling Language. The current paper work is to the associate degree carrying out of Apriori algorithmic rule towards the information of EBDS.

M. Angulakshmi

Papers

Automated brain tumour segmentation techniques- A review

Walsh Hadamard Transform for Simple Linear Iterative Clustering (SLIC) Superpixel Based Spectral Clustering of Multimodal MRI Brain Tumor Segmentation

A Review on Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation.

Walsh Hadamard kernel‐based texture feature for multimodal MRI brain tumour segmentation

Association Rule Modeling using UML and Apriori Algorithm