Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches (original) (raw)
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Automated detection of multiple sclerosis lesions in the human brain using MR image processing
Neuroscience Letters, 1997
Introduction Multiple sclerosis (MS) is a serious disease typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Magnetic resonance imaging (MRI) is frequently used in serial brain imaging due to the rich and detailed information provided. Methods Time-series analysis of images is widely used for MS diagnosis and patient follow-up. However, conventional manual methods are time-consuming, subjective, and errorprone. Thus, the development of automated techniques for the detection and quantification of MS lesions is a major challenge. Results This paper presents an up-to-date review of the approaches which deal with the time-series analysis of brain MRI for detecting active MS lesions and quantifying lesion load change. We provide a comprehensive reference source for researchers in which several approaches to change detection and quantification of MS lesions are investigated and classified. We also analyze the results provided by the approaches, discuss open problems, and point out possible future trends. Conclusion Lesion detection approaches are required for the detection of static lesions and for diagnostic purposes, while either quantification of detected lesions or change detection algorithms are needed to follow up MS patients. However, there is not yet a single approach that can emerge as a standard for the clinical practice, automatically providing an accurate MS lesion evolution quantification. Future trends will focus on combining the lesion detection in single studies with the analysis of the change detection in serial MRI.
Automated detection of multiple sclerosis lesions in serial brain MRI
Neuroradiology, 2011
INTRODUCTION: Multiple sclerosis (MS) is a serious disease typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Magnetic resonance imaging (MRI) is frequently used in serial brain imaging due to the rich and detailed information provided. METHODS: Time-series analysis of images is widely used for MS diagnosis and patient follow-up. However, conventional manual methods are time-consuming, subjective, and error-prone. Thus, the development of automated techniques for the detection and quantification of MS lesions is a major challenge. RESULTS: This paper presents an up-to-date review of the approaches which deal with the time-series analysis of brain MRI for detecting active MS lesions and quantifying lesion load change. We provide a comprehensive reference source for researchers in which several approaches to change detection and quantification of MS lesions are investigated and classified. We also analyze the results provided by the approa...
The neuroradiology journal, 2013
Multiple sclerosis (MS) is a chronic disease with a progressing and evolving course. Serial imaging with MRI is the mainstay in monitoring and managing MS patients. In this work we demonstrate the performance of a locally developed computer-assisted detection (CAD) software used to track temporal changes in brain MS lesions. CAD tracks changes in T2-bright MS lesions between two time points on a 3D high-resolution isotropic FLAIR MR sequence of the brain acquired at 3 Tesla. The program consists of an image-processing pipeline, and displays scrollable difference maps used as an aid to the neuroradiologist for assessing lesional change. To assess the value of the software we have compared diagnostic accuracy and duration of interpretation of the CAD-assisted and routine clinical interpretations in 98 randomly chosen, paired MR examinations from 88 patients (68 women, 20 men, mean age 43.5, age range 21-75) with a diagnosis of definite MS. The ground truth was determined by a three-ex...
Statistical Analysis of Longitudinal MRI Data: Applications for Detection of Disease Activity in MS
Lecture Notes in Computer Science, 2002
We present a method to detect intensity changes in longitudinal volumetric MRI data from patients with multiple sclerosis (MS). Preprocessing includes spatial and intensity normalization. The intrasubject intensity normalization is achieved using a polynomial least trimmed squares method to match the histograms of all images in the series. Viewing the detection of disease activity in MRI as a change-point problem, we present two statistical tests and apply them to a patient's series of grey-level images on a voxel-by-voxel basis. Results are compared with manual lesion segmentation for one MS patient scanned approximately every 5 months for 5 years. Results are also shown for 12 MS patients with 30 monthly scans.
Automated Detection and Characterization of Multiple Sclerosis Lesions in Brain MR Images
Magnetic Resonance Imaging, 1998
In the present study an automatic algorithm for detection and contouring of multiple sclerosis (MS) lesions in brain magnetic resonance (MR) images is introduced. This algorithm automatically detects MS lesions in axial proton density, T 2 -weighted, gadolinium enhanced, and fast fluid attenuated inversion recovery (FLAIR) brain MR images. Automated detection consists of three main stages: (1) detection and contouring of all hyperintense signal regions within the image; (2) partial elimination of false positive segments (defined herein as artifacts) by size, shape index, and anatomical location; (3) the use of an artificial neural paradigm (Back-Propagation) for final removal of artifacts by differentiating them from true MS lesions. The algorithm was applied to 45 images acquired from 14 MS patients. The algorithm's sensitivity was 0.87 and the specificity 0.96. In 34 images, 100% of the lesions were detected. The algorithm potentially may serve as a useful preprocessing tool for quantitative MS monitoring via magnetic resonance imaging.
Computer Methods and Programs in Biomedicine, 2017
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights Accurate detection and segmentation of Multiple sclerosis (MS) diseases with lesions positions identification Adaptive background generation and binarization using global threshold are the key steps for MS lesions detection Evaluates performance with other recent method Proposed method produce good results visually as well as metrically Proposed method reduced the under segmentation, over segmentation, and spurious lesions generation
A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs
arXiv (Cornell University), 2023
Introduction: Multiple Sclerosis (MS) is a chronic disease that affects millions of people across the globe. MS can critically affect different organs of the central nervous system such as the eyes, the spinal cord, and the brain. Background: To help physicians in diagnosing MS lesions, computer-aided methods are widely used. In this regard, a considerable research has been carried out in the area of automatic detection and segmentation of MS lesions in magnetic resonance images (MRIs). Methodology: In this study, we review the different approaches that have been used in computer-aided detection and segmentation of MS lesions. Our review resulted in categorizing MS lesion segmentation approaches into six broad categories: data-driven, statistical, supervised machine learning, unsupervised machine learning, fuzzy, and deep learning-based techniques. We critically analyze the different techniques under these approaches and highlight their strengths and weaknesses.
2016
Synopsis: Magnetic Resonance Imaging (MRI) plays an important role for lesion assessment in early stages of Multiple Sclerosis (MS). This work aims at evaluating the performance of an automated tool for MS lesion detection, segmentation and tracking in longitudinal data, only for use in this research study. The method was tested with images acquired using both a "clinical" and an "advanced" imaging protocol for comparison. The validation was conducted in a cohort of thirty-two early MS patients through a ground truth obtained from manual segmentations by a neurologist and a radiologist. The use of the "advanced protocol" significantly improves lesion detection and classification in longitudinal analyses.
Automatic Segmentation of Multiple Sclerosis Lesions in Brain MR Images
Journal of Biomedical Engineering and Medical Imaging, 2015
We present, in the scope of this article, a contribution to the automatic extraction of Multiple Sclerosis (MS) lesions from MRI images (Magnetic Resonance Imaging). Our method is entirely automatic[1][2]. It is based on three steps : first, brain segmentation, then construction of Talairach atlas thanks to the determination of the CA-CP, VCA, and the interhemispheric plans, and finally the extraction of MS lesions by statistic analysis. Thus, the results we have obtained are close to 100%, even if some mistakes, linked to unexpected movements of the patient, can occur during the acquisition.