Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging

Danelakis, Antonios; Theoharis, Theoharis; Verganelakis, Dimitrios

doi:10.1016/j.compmedimag.2018.10.002

Cited by 90 publications

(74 citation statements)

References 107 publications

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“…The first categorization is detailed by Danelakis and co-workers [11]. The categorization is based more on supervised and unsupervised methods, 2D and 3D volume based, than on the strategy used.…”

Section: U R R E N T Tr E N D S I N M R I -C a D Scheme Methodsmentioning

confidence: 99%

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Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Hameurlaine¹,

Moussaoui²

2019

Nano BioMed ENG

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Brain tumor extraction is challenging task because brain image and its structure are complicated that can be analyzed only by expert physicians or radiologist. Brain tumor detection and segmentation is one of the most challenging and time consuming task in medical image processing. The image segmentation is a very difficult job in the image processing and challenging task for clinical diagnostic tools. MRI (Magnetic Resonance Imaging) is a visualization medical technique, which provides plentiful information about the human soft tissue, which helps in the diagnosis of brain tumor. Accurate segmentation of the MRI images is extremely important and essential for the exact diagnosis by computer aided clinical tools. There are different types of segmentation algorithms for MRI brain images. This paper is to check existing approaches of Brain tumor segmentation techniques in MRI image for Computer aided diagnosis.

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Section: U R R E N T Tr E N D S I N M R I -C a D Scheme Methodsmentioning

confidence: 99%

“…In fact, there is permanent debate about the best method to evaluate performance of results. Table 6 summarizes the most common measures used to evaluate the MS lesion segmentation algorithms [11,19]. Table 7 summarizes some evaluation results in the MS lesion segmentation algorithms.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Hameurlaine¹,

Moussaoui²

2019

Nano BioMed ENG

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“…Various published automatic techniques for segmenting MS lesions have been reviewed . These two reviews have clearly articulated the strengths, weaknesses, and general limitations of various automatic segmentation methods.…”

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confidence: 99%

“…Thus, performance of these automatic segmentation techniques appears suboptimal, especially when the data are acquired at different centers and different scanners . Therefore, there is a need for automatic segmentation techniques that provide accurate results on data acquired at multiple centers using different scanner platforms.…”

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confidence: 99%

Deep‐Learning‐Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size

Narayana

Coronado

Sujit

et al. 2019

Magnetic Resonance Imaging

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Background: The dependence of deep-learning (DL)-based segmentation accuracy of brain MRI on the training size is not known. Purpose: To determine the required training size for a desired accuracy in brain MRI segmentation in multiple sclerosis (MS) using DL. Study Type: Retrospective analysis of MRI data acquired as part of a multicenter clinical trial. Study Population: In all, 1008 patients with clinically definite MS. Field Strength/Sequence: MRIs were acquired at 1.5T and 3T scanners manufactured by GE, Philips, and Siemens with dual turbo spin echo, FLAIR, and T 1 -weighted turbo spin echo sequences. Assessment: Segmentation results using an automated analysis pipeline and validated by two neuroimaging experts served as the ground truth. A DL model, based on a fully convolutional neural network, was trained separately using 16 different training sizes. The segmentation accuracy as a function of the training size was determined. These data were fitted to the learning curve for estimating the required training size for desired accuracy. Statistical Tests: The performance of the network was evaluated by calculating the Dice similarity coefficient (DSC), and lesion true-positive and false-positive rates. Results: The DSC for lesions showed much stronger dependency on the sample size than gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). When the training size was increased from 10 to 800 the DSC values varied from 0.00 to 0.86 AE 0.016 for T 2 lesions, 0.87 AE 009 to 0.94 AE 0.004 for GM, 0.86 AE 0.08 to 0.94 AE 0.005 for WM, and 0.91 AE 0.009 to 0.96 AE 0.003 for CSF. Data Conclusion: Excellent segmentation was achieved with a training size as small as 10 image volumes for GM, WM, and CSF. In contrast, a training size of at least 50 image volumes was necessary for adequate lesion segmentation.

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“…Dieses, wie auch die dort aufgeführten Arbeiten, fußen auf konventionellen Programmiermethoden. Eine neuere Übersichtsarbeit, die sich speziell dem Thema Läsionssegmentierung widmet und auch KI-Studien berücksichtigt, legten Danelakis et al vor [15]. Ein Beispiel einer solchen aktuellen Studie stammt von Li et al [16].…”

Section: Literaturergebnisse: Anwendung Von Ki Bei Multipler Skleroseunclassified

AI in Radiology: Where are we today in Multiple Sclerosis Imaging?

Eichinger

Zimmer

Wiestler

2020

Rofo

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Background MR imaging is an essential component in managing patients with Multiple sclerosis (MS). This holds true for the initial diagnosis as well as for assessing the clinical course of MS. In recent years, a growing number of computer tools were developed to analyze imaging data in MS. This review gives an overview of the most important applications with special emphasis on artificial intelligence (AI). Methods Relevant studies were identified through a literature search in recognized databases, and through parsing the references in studies found this way. Literature published as of November 2019 was included with a special focus on recent studies from 2018 and 2019. Results There are a number of studies which focus on optimizing lesion visualization and lesion segmentation. Some of these studies accomplished these tasks with high accuracy, enabling a reproducible quantitative analysis of lesion loads. Some studies took a radiomics approach and aimed at predicting clinical endpoints such as the conversion from a clinically isolated syndrome to definite MS. Moreover, recent studies investigated synthetic imaging, i. e. imaging data that is not measured during an MR scan but generated by a computer algorithm to optimize the contrast between MS lesions and brain parenchyma. Conclusion Computer-based image analysis and AI are hot topics in imaging MS. Some applications are ready for use in clinical routine. A major challenge for the future is to improve prediction of expected disease courses and thereby helping to find optimal treatment decisions on an individual level. With technical improvements, more questions arise about the integration of new tools into the radiological workflow. Key Points: Citation Format

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Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging

Cited by 90 publications

References 107 publications

Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Deep‐Learning‐Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size

AI in Radiology: Where are we today in Multiple Sclerosis Imaging?

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