Multiple Sclerosis Lesion Segmentation Using an Automatic Multimodal Graph Cuts

García-Lorenzo, Daniel; Lecoeur, Jérémy; Arnold, Douglas L.; Collins, D. Louis; Barillot, Christian

doi:10.1007/978-3-642-04271-3_71

Cited by 34 publications

(45 citation statements)

References 16 publications

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“…In addition, some metrics are sensitive to the surface-to-volume ratio of the structures segmented; therefore, it may be important to take lesion size into account to correctly understand the results. Only 14 papers included the lesion load of the patients, and the range varied greatly among the papers; the lowest lesion loads ranged from approximately 1 cm 3 (Alfano et al, 2000;García-Lorenzo et al, 2009;Harmouche et al, 2006) to 8 cm 3 (Shiee et al, 2009), and the highest, from 20.0 cm 3 to 130 cm 3 (Alfano et al, 2000). Some authors divided the patients according to lesion load (Khayati et al, 2008a;Sajja et al, 2006), although no consensus exists on how to achieve this division.…”

Section: Characteristics Of the Databasementioning

confidence: 99%

“…Other approaches considered the manual segmentation imperfect and merged several manual segmentations to create a silver standard and reduce the variability of each expert individually (García-Lorenzo et al, 2009;Harmouche et al, 2006;Subbanna et al, 2009). This silver standard has been created both by using a consensus approach, whereby each voxel is considered lesion only if most experts considered it lesion, and by using the STAPLE algorithm (Warfield et al, 2004), whereby sensitivity and specificity are computed simultaneously with the silver standard.…”

Section: The Ground Truthmentioning

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

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319

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: Characteristics Of the Databasementioning

confidence: 99%

Section: The Ground Truthmentioning

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

Self Cite

319

268

View full text Add to dashboard Cite

show abstract

“…We note that the selected voxels are not just outliers with respect to the tissue model, but also with respect to the MRF and prior atlas model. The candidate region is refined using additional intensity constraints, as in [14,6,7].…”

Section: Resultsmentioning

confidence: 99%

“…To our knowledge, very few other BrainWeb MS results exist in the literature. A recent paper [7] provides results for all lesion loads, three non-uniformity levels and the above levels of noise. Another paper [10] reports results for the three lesion loads but only in the 3% noise 0% nonuniformity case while [6] reports DSC values for all above levels of noise but only in the moderate lesion case.…”

Section: Resultsmentioning

confidence: 99%

Adaptive weighted fusion of multiple MR sequences for brain lesion segmentation

Forbes

Doyle

García-Lorenzo

et al. 2010

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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International audienceWe propose a technique for fusing the output of multiple Magnetic Resonance (MR) sequences to robustly and accurately segment brain lesions. It is based on a Bayesian multi-sequence Markov model that includes weight parameters to account for the relative importance and control the impact of each sequence. The Bayesian framework has the advantage of allowing 1) the incorporation of expert knowledge on the a priori relevant information content of each sequence and 2) a weighting scheme which is modified adaptively according to the data and the segmentation task under consideration. The model, applied to the detection of multiple sclerosis and stroke lesions shows promising results

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“…Some of these MS lesion regions that are located in uncommon locations can be eliminated. Odd locations include MS lesions outside the brain area, close to the brain boundary, or close to the sagittal plan [27]. In Figure 8, step 1 of post processing is applied to the initial segmentation of a slice from subject MS6, shown in Figure 8(a) and color evaluated in Figure 8(b), to eliminate the erroneous MS lesion regions circled in yellow circle as they are located so close to the boundary of the slice.…”

Section: Post Processingmentioning

confidence: 99%

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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Multiple Sclerosis Lesion Segmentation Using an Automatic Multimodal Graph Cuts

Cited by 34 publications

References 16 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

Adaptive weighted fusion of multiple MR sequences for brain lesion segmentation

Textural Based SVM for MS Lesion Segmentation in FLAIR MRIs

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