Segmentation of multiple sclerosis lesions in MR images: a review

Abstract

Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels.

Improving Multiple Sclerosis Plaque Detection Using a Semiautomated Assistive Approach

“…9 To facilitate time-efficient, reproducible, and accurate lesion-load detection, many algorithms have been proposed for fully automated computer-assistive solutions. 3,18 These methods use different principles, including intensity-gradient features, 19 intensity thresholding, 20 intensity-histogram modeling of expected tissue classes, [21][22][23] fuzzy connectedness, 24 identification of nearest neighbors in a feature space, 25,26 or a combination of these. Methods such as Bayesian inference, expectation maximization, support-vector machines, k-nearest neighbor majority voting, and artificial neural networks are algorithmic approaches used to op- Comparing the number of study pairs improved with demyelinating lesions detected by both readers when using the newly developed assistive software to the issued radiology report.…”

“…[2][3][4][5][6] Recent advances in imaging, including 3T 3D volumetric T2 FLAIR sequences, allow better resolution of small demyelinating lesions, resulting in better clinicoradiologic correlation. 7,8 Despite advances in imaging techniques, conventional side-by-side comparison (CSSC) is often subject to a reader's expertise.…”

“…10 A review of fully automated MS segmentation techniques concluded that basic data-driven methods are inherently inaccurate; supervised learning methods (such as artificial neural networks) require costly and extensive training on representative data; deformable models are better; and statistical models are most promising, though these also require training on representative data. 3 An alternative to total automation is to assist manual reporting with partial automation. A few semiautomated lesion-subtraction strategies have been used in the research setting on small patient populations with good lesion detection and interreader correlation.…”

Agent-based intelligent medical diagnosis system for patients

“…The early contributions can be found on the neural networks, it provides a new significant way for intelligent medical diagnosis. Based on this idea, artificial neural networks have been applied in the diagnosis of: (i) pancreatic disease [1], (ii) gynecological diseases [2], (iii) early diabetes [3], (iv) colorectal cancer [4], and (v) multiple sclerosis lesions [5]. While this kind of method to set up the achievements of medical diagnostic system is still limited, the main reasons are that the learning algorithm cannot calculate the right results when the required algorithm to set up neural network model solves the larger, multi-features disease diagnosis problems.…”

Segmentation of Brain MRI

“…For brain MR image segmentation, some studies aim to identify the entire image into subregions such as white matter (WM), grey matter (GM), and cerebrospinal fluid spaces (CSF) of the brain (Lim & Pfefferbaum, 1989), whereas others aim to extract one specific structure, for instance, brain tumour (M.C. Clark et al, 1998), multiple sclerosis lesions (Mortazavi et al, 2011), or subcortical structures (Babalola et al, 2008). Due to varying complications in segmenting human cerebral cortex, the manual methods for brain tissues segmentation might easily lead to errors both in accuracy and reproducibility (operator bias), and are exceedingly time-consuming, we thus need fast, accurate and robust semi-automatic (i.e., supervised classification explicitly needs user interaction) or completely automatic (i.e., nonsupervised classification) techniques (Suri, Singh, et al, 2002b).…”