A deep supervised approach for ischemic lesion segmentation from multimodal MRI using Fully Convolutional Network

Karthik, R.; Gupta, Utkarsh; Jha, Ashish; Rajalakshmi, R.; Menaka, R.

doi:10.1016/j.asoc.2019.105685

Cited by 63 publications

(30 citation statements)

References 51 publications

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“…The preprocessing steps commonly used are (1) grayscale conversion and image resizing [167] to get better contrast and enhancement; (2) bias field correction to correct the intensity inhomogeneity [30,166]; (3) image registration, a process for spatial alignment [166]; and (4) removal of nonbrain tissue such as fat, skull, or neck, which has intensities overlapping with intensities of brain tissues [27,166,168].…”

Section: Image Preprocessingmentioning

confidence: 99%

“…Even though there are a large number of publications related to ischemic stroke (27 documents) and most deal with the classification of stroke patients versus normal controls, or the prediction of post-stroke functional impairment or treatment outcome [21,25,26,28,33,38,42,66,67,72,74,75,77,78,80,167,168,179,[184][185][186][187][188][189][190][193][194][195], there is a paucity of results related to demyelinating diseases alone. However, there are some publications dealing with multiple sclerosis (MS), which is the most common demyelinating disease (2) [191,192].…”

Section: And DL Classifiers Applied To Diagnosis Of Ischemia and Dmentioning

confidence: 99%

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MR Images, Brain Lesions, and Deep Learning

2021

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Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment.

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Section: Image Preprocessingmentioning

confidence: 99%

Section: And DL Classifiers Applied To Diagnosis Of Ischemia and Dmentioning

confidence: 99%

MR Images, Brain Lesions, and Deep Learning

2021

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“…The preprocessing steps commonly used are (1) the grayscale conversion, and the image resizing [161] to get better contrast and enhancement, (2) bias field correction to correct the intensity inhomogeneity [24,160], (3) image registration, a process for spatial alignment [160], and (4) removal of nonbrain tissue such as fat, skull, or neck which have intensities overlapping with intensities of brain tissues [21,160,162].…”

Section: Image Preprocessingmentioning

confidence: 99%

MRI Images, Brain Lesions and Deep Learning

Castillo¹,

Lakshminarayanan²,

Rodríguez-Álvarez³

2021

Preprint

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Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment.

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“…The second best performing method use a modified level-set approach embedded with the fuzzy C-means algorithm [34] while the third best method is based on random forests and contextual clustering [35]. R. Karthika's team proposed a U-NET improvement model to segment the ischemic stroke lesion on the multimodal MRI [36].…”

Section: Related Workmentioning

confidence: 99%

Ischemic Stroke Lesion Segmentation Using Multi-Plane Information Fusion

et al. 2020

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Diffusion-weighted magnetic resonance imaging (DWI) is sensitive to acute ischemic stroke and is a common diagnostic method for the stroke. However, the diagnostic result relies on the visual observation of neurologists which may vary from doctor to doctor under different circumstance. And manual segmentation is often a time-consuming and subjective process. The time from onset to thrombus removal has a significant impact on the prognosis of patients with acute ischemic stroke. The shorter the time, the better the prognosis. For this purpose we present a novel framework to quickly and automatically segment the ischemic stroke lesions on DWI. We mainly have three contributions: firstly, we design a detection and segmentation network (DSN) to solve the two kinds of data imbalance; secondly, we propose a triple-branch DSN architecture, used for extracting different plane feature respectively; thirdly, we propose a multi-plane fusion network (MPFN), which aims to make final prediction more accurate. Extensive experiments on ISLES2015 SSIS DWI sequence dataset demonstrate the superiority of our proposed segmentation method. The dice reached 62.2% and the sensitivity reached 71.7%.

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A deep supervised approach for ischemic lesion segmentation from multimodal MRI using Fully Convolutional Network

Cited by 63 publications

References 51 publications

MR Images, Brain Lesions, and Deep Learning

MR Images, Brain Lesions, and Deep Learning

MRI Images, Brain Lesions and Deep Learning

Ischemic Stroke Lesion Segmentation Using Multi-Plane Information Fusion

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