State-of-the-Art Methods for Brain Tissue Segmentation: A Review

Dora, Lingraj; Agrawal, Sanjay; Panda, Rutuparna; Abraham, Ajith

doi:10.1109/rbme.2017.2715350

Cited by 90 publications

(34 citation statements)

References 109 publications

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“…An impressive range of segmentation methods and approaches have been reported (especially for brain segmentation) and reviewed, e.g. [254,255,256,257,258,259,260,261,262]. MR image segmentation using deep learning approaches, typically CNNs, are now penetrating the whole field of applications.…”

Section: Image Segmentationmentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,591

883

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What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging. Artificial neural networksArtificial neural networks (ANNs) is one of the most famous machine learning models, introduced already in the 1950s, and actively studied since [41, Chapter 1.2]. 11 Roughly, a neural network consists of a number of connected computational units, called neurons, arranged in layers. There's an input layer where data enters the network, followed by one or more hidden layers transforming the data as it flows through, before ending at an output layer that produces the neural network's predictions. The network is trained to output useful predictions by identifying patterns in a set of labeled training data, fed through the network while the outputs are compared with the actual labels by an objective function. During training the network's parametersthe strength of each neuron-is tuned until the patterns identified by the network result in good 10 https://www.deeplearningbook.org/ 11 The loose connection between artificial neural networks and neural networks in the brain is often mentioned, but quite over-blown considering the complexity of biological neural networks. However, there is some interesting recent work connecting neuroscience and artificial neural networks, indicating an increase in the cross-fertilization between the two fields [43,44,45].3 predictions for the training data. Once the patterns are learned, the network can be used to make predictions on new, unseen data, i.e. generalize to new data.It h...

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Section: Image Segmentationmentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,591

883

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“…The merits and demerits of the existing brain tissue segmentation methods are discussed in [28], which offer insight into the comparative performance of methods based on clustering, thresholding, convolutional neural networks (CNNs), and Markov Chain Monte Carlo (MCMCs).…”

Section: Computational Experimentsmentioning

confidence: 99%

Discovery and visualization of structural biomarkers from MRI using transport-based morphometry

et al. 2018

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Disease in the brain is often associated with subtle, spatially diffuse, or complex tissue changes that may lie beneath the level of gross visual inspection, even on magnetic resonance imaging (MRI). Unfortunately, current computer-assisted approaches that examine pre-specified features, whether anatomically-defined (i.e. thalamic volume, cortical thickness) or based on pixelwise comparison (i.e. deformation-based methods), are prone to missing a vast array of physical changes that are not well-encapsulated by these metrics. In this paper, we have developed a technique for automated pattern analysis that can fully determine the relationship between brain structure and observable phenotype without requiring any a priori features. Our technique, called transport-based morphometry (TBM), is an image transformation that maps brain images loss-lessly to a domain where they become much more separable. The new approach is validated on structural brain images of healthy older adult subjects where even linear models for discrimination, regression, and blind source separation enable TBM to independently discover the characteristic changes of aging and highlight potential mechanisms by which aerobic fitness may mediate brain health later in life. TBM is a generative approach that can provide visualization of physically meaningful shifts in tissue distribution through inverse transformation. The proposed framework is a powerful technique that can potentially elucidate genotype-structural-behavioral associations in myriad diseases.

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“…E. Goceri et al [10] have done a comprehensive survey on recent advances and future trends of Deep Learning (DL) in medical image analysis and stated that segmentation is the most common technique applied in medical image analysis using deep learning. Moreover [11], has discussed state-of-the-art methods for brain tissue segmentation like manual, region-based, thresholdingbased, clustering-based, and feature extraction methods. A comprehensive review is done by [12] about the medical image segmentation on GPUs (Graphical Processing Unit).…”

Section: Introductionmentioning

confidence: 99%

A novel technique for analysing histogram equalized medical images using superpixels

Yao

Sohail

2019

Computer Assisted Surgery

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We present a novel technique to distinguish between an original image and its histogram equalized version. Histogram equalization and superpixel segmentation such as SLIC (simple linear iterative clustering) are very popular image processing tools. Based on these two concepts, we introduce a method for finding whether an image (grayscale) is histogram equalized or not. Because sometimes we see images that look visually similar but they are actually processed or changed by some image enhancement process such as histogram equalization. We can merely infer whether the image is dark, bright or has a small dynamic range. Moreover, we also compare the result of SLIC superpixels with three other superpixel segmentation algorithms namely, quick shift, watersheds, and Felzenszwalb's segmentation algorithm

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State-of-the-Art Methods for Brain Tissue Segmentation: A Review

Cited by 90 publications

References 109 publications

An overview of deep learning in medical imaging focusing on MRI

An overview of deep learning in medical imaging focusing on MRI

Discovery and visualization of structural biomarkers from MRI using transport-based morphometry

A novel technique for analysing histogram equalized medical images using superpixels

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