Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine

Yamamoto, Daisuke; Arimura, Hidetaka; Kakeda, Shingo; Magome, Taiki; Yamashita, Yasuo; Fukai, Tomoki; Ohki, Masafumi; Higashida, Yoshiharu; Korogi, Yukunori

doi:10.1016/j.compmedimag.2010.02.001

Cited by 62 publications

(46 citation statements)

References 34 publications

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“…The artefacts were then discarded in a second stage by the ANN using as input the shape and intensity of the candidate lesion. Yamamoto et al (Yamamoto et al, 2010) employed a multithreshold technique to detect candidate lesions and then a level set to improve the border of the candidate WML. Finally, false positives were rejected with an SVM that used shape and intensity features.…”

Section: Supervised Learning Methodsmentioning

confidence: 99%

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Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

García-Lorenzo

Francis

Narayanan

et al. 2013

Medical Image Analysis

306

268

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Magnetic resonance (MR) imaging is often used to characterize and quantify multiple sclerosis (MS) lesions in the brain and spinal cord. The number and volume of lesions have been used to evaluate MS disease burden, to track the progression of the disease and to evaluate the effect of new pharmaceuticals in clinical trials. Accurate identification of MS lesions in MR images is extremely difficult due to variability in lesion location, size and shape in addition to anatomical variability between subjects. Since manual segmentation requires expert knowledge, is time consuming and is subject to intra-and interexpert variability, many methods have been proposed to automatically segment lesions.The objective of this study was to carry out a systematic review of the literature to evaluate the state of the art in automated multiple sclerosis lesion segmentation. From 1240 hits found initially with PubMed and Google scholar, our selection criteria identified 80 papers that described an automatic lesion segmentation procedure applied to MS. Only 47 of these included quantitative validation with at least one realistic image. In this paper, we describe the complexity of lesion segmentation, classify the automatic MS lesion segmentation methods found, and review the validation methods applied in each of the papers reviewed. Although many segmentation solutions have been proposed, including some with promising results using MRI data obtained on small groups of patients, no single method is widely employed due to performance issues related to the high variability of MS lesion appearance and differences in image acquisition. The challenge remains to provide segmentation techniques that work in all cases regardless of the type of MS, duration of the disease, or MRI protocol, and this within a comprehensive, standardized validation framework. MS lesion segmentation remains an open problem.

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Section: Supervised Learning Methodsmentioning

confidence: 99%

“…Other authors computed the sensitivity and specificity, but used the lesion counts instead of the voxel labels Styner et al, 2008;Yamamoto et al, 2010).…”

Section: -Lesion-based Measuresmentioning

confidence: 99%

Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

García-Lorenzo

Francis

Narayanan

et al. 2013

Medical Image Analysis

306

268

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“…DS R uses the number of common MS lesion regions between man-ual and automatic segmentation for X Y  and uses the number of MS lesion regions of manual and auto-matic segmentation for X and Y respectively. In the context of DS R , the automatically segmented lesion region that shares at least one pixel with a manually segmented lesion region is considered as a common MS lesion region since the number of MS lesion regions is more clinically relevant than the number of voxels [4]. For example, applying these definitions of DS V and DS R to the initial segmentations shown in Figures 11(i1) and (i2) yields values of 0.73 (good segmentation) and 1.0 (perfect segmentation), respectively.…”

Section: Evaluation Of the Proposed Methodsmentioning

confidence: 99%

“…Due to its sensitivity in detecting MS lesions, Magnetic Resonance Imaging (MRI) has become an effective tool for diagnosing MS and monitoring its progression [2,3]. Accurate manual assessment of each lesion in MR images would be a demanding and laborious task, and would also be subjective and have poor reproducibility [4]. Automatic Segmentation offers an attractive alternative to manual segmentation which remains a time-consuming task and suffers from intra-and inter-expert variability [5].…”

Section: Introductionmentioning

confidence: 99%

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Textural Based SVM for MS Lesion Segmentation in FLAIR MRIs

Abdullah¹,

Younis²,

Pattany³

et al. 2011

OJMI

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In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI). The technique uses textural features to describe the blocks of each MRI slice along with position and neighborhood features. A trained support vector machine (SVM) is used to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions based on mainly the textural features with aid of the other features. The MRI slice blocks' classification is used to provide an initial segmentation. A comprehensive post processing module is then utilized to refine and improve the quality of the initial segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated process without the need to manually define regions of interest (ROIs). In addition, the post processing module is generic enough to be applied to the results of any other MS segmentation technique to improve the segmentation quality. This technique is evaluated using ten real MRI data-sets with 10% used in the training of the textural-based SVM. The average results for the performance evaluation of the presented technique were 0.79 for dice similarity, 0.68 for sensitivity and 0.9 for the percentage of the detected lesion load. These results indicate that the proposed method would be useful in clinical practice for the detection of MS lesions from MRI.

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Automated detection of white matter and cortical lesions in early stages of multiple sclerosis

Fartaria

Bonnier

Roche

et al. 2015

Magnetic Resonance Imaging

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Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine

Cited by 62 publications

References 34 publications

Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

Textural Based SVM for MS Lesion Segmentation in FLAIR MRIs

Automated detection of white matter and cortical lesions in early stages of multiple sclerosis

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