Towards automatic MRI volumetry for treatment selection in acute ischemic stroke patients

Bauer, Stefan; Gratz, Pascal P.; Gralla, Jan; Wiest, Roland

doi:10.1109/embc.2014.6943891

Cited by 7 publications

(9 citation statements)

References 16 publications

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“…A few of these datasets were obtained from the hospital; datasets from General Hospital of Kuala Lumpur for research conducted by [47], [65], [66]. All these datasets for segmenting core and penumbra were using DWI and PWI sequence [23] - [28]. The proposed algorithms [25], [27], [28], [53], [69], [70] listed in Table 1 have produced a segmentation outcome within the allocated time recommended by FDA's stroke protocol.…”

Section: Comparison Of Acute Ischemic Stroke Segmentation Methods Basmentioning

confidence: 99%

“…All these datasets for segmenting core and penumbra were using DWI and PWI sequence [23] - [28]. The proposed algorithms [25], [27], [28], [53], [69], [70] listed in Table 1 have produced a segmentation outcome within the allocated time recommended by FDA's stroke protocol. The goal of producing the automated segmentation is to reduce the time taken of the radiologist's interpretation for a single patient.…”

Section: Comparison Of Acute Ischemic Stroke Segmentation Methods Basmentioning

confidence: 99%

“…The approaches include region growing, contour-based, thresholding and even hybrid-based method. One of the challenges that was addressed was most of these methods were successfully segment the infarct core but did not focus on isolating the penumbra and infarct core separately [23] - [28]. Figure 1 illustrates generic brain image segmentation process flow.…”

Section: Generic Brain Ischemic Stroke Segmentation Process Flowmentioning

confidence: 99%

“…The algorithm was tested on SPES dataset and achieved an average Dice Coefficient (DC) of 0.82 in 6 minutes. Bauer et al [28] employed energy minimization function approach to segment infarct core and penumbra in stroke patients and have produced dice coefficient of penumbra by 0.61. Wu et al [24] developed a thresholding and generalized linear model (GLM) algorithm to predict tissue outcome of infarct core and penumbra tissues combining DWI and PWI datasets.…”

Section: Table 1 -Performance Comparison Of the Acute Ischemic Strokementioning

confidence: 99%

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A Review of MRI Acute Ischemic Stroke Lesion Segmentation

Isa

Kipli

Mahmood

et al. 2020

IJIE

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Immediate treatment of a stroke can minimize long-term effects and even help reduce death risk. In the ischemic stroke cases, there are two zones of injury which are ischemic core and ischemic penumbra zone. The ischemic penumbra indicates the part that is located around the infarct core that is at risk of developing a brain infarction. Recently, various segmentation methods of infarct lesion from the MRI input images were developed and these methods gave a high accuracy in the extraction and detection of the infarct core. However, only some limited works have been reported to isolate the penumbra tissues and infarct core separately. The challenges exist in ischemic core identification are traditional approach prone to error, time-consuming and tedious for medical expert which could delay the treatment. In this paper, we study and analyse the segmentation algorithms for brain MRI ischemic of different categories. The focus of the review is mainly on the segmentation algorithms of infarct core with penumbra and infarct core only. We highlight the advantages and limitations alongside the discussion of the capabilities of these segmentation algorithms and its key challenges. The paper also devised a generic structure for automated stroke lesion segmentation. The performance of these algorithms was investigated by comparing different parameters of the surveyed algorithms. In addition, a new structure of the segmentation process for segmentation of penumbra is proposed by considering the challenges remains. The best accuracy for segmentation of infarct core and penumbra tissues is 82.1% whereas 99.1% for segmentation infarct core only. Meanwhile, the shortest average computational time recorded was 3.42 seconds for segmenting 10 slices of MR images. This paper presents an inclusive analysis of the discussed papers based on different categories of the segmentation algorithm. The proposed structure is important to enable a more robust and accurate assessment in clinical practice. This could be an opportunity for the medical and engineering sector to work together in designing a complete end-to-end automatic framework in detecting stroke lesion and penumbra.

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Section: Comparison Of Acute Ischemic Stroke Segmentation Methods Basmentioning

confidence: 99%

Section: Comparison Of Acute Ischemic Stroke Segmentation Methods Basmentioning

confidence: 99%

Section: Generic Brain Ischemic Stroke Segmentation Process Flowmentioning

confidence: 99%

Section: Table 1 -Performance Comparison Of the Acute Ischemic Strokementioning

confidence: 99%

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A Review of MRI Acute Ischemic Stroke Lesion Segmentation

Isa

Kipli

Mahmood

et al. 2020

IJIE

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“…The blockage of the blood vessel and the bursts of the blood vessels causes the brain stroke [1][2]. There are three main kinds of stroke: Ischemic strokes (severely reduced blood flow), Hemorrhagic strokes (leakage of blood) and Transient ischemic attacks (TIAs-also referred to as mini-strokes) [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Crossbred Approach for Effective Brain Stroke Lesion Segmentation

Melingi¹,

Vivekanand²

2018

IJIES

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An Ischemic stroke is expressed as lost neurological brain work because of the sudden loss of blood dissemination in the specific territory of the brain. The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. In this paper we utilize a hybrid way to deal with detecting the ischemic stroke from the alternate pathologies in magnetic resonance (MR) images utilizing Kernelized Fuzzy C-means (KFCM) clustering with adaptive threshold algorithm and the Support Vector Machine (SVM) classifier. In the existing method, the Otsu's method incorporated with SVM classifier method is utilized for the segmentation of the ischemic stroke image, but it has the limited accuracy 88%, specificity 66% and the sensitivity value is 94%. For the exact identification and segmentation the KFCM algorithm is utilized. The distance and the intensity of the lesion tissue is identified by this method. The accuracy and segmentation aftereffects of the classifier are measured in the testing and training phase by looking at the comparable and a decent variety of sample sets by considering diverse groupings. Our test comes about demonstrating that, the performance of the proposed technique is assessed in view of the precision, recall, sensitivity, accuracy and overlap metrics of the framework. Compared with the existing classification method, the proposed method has 17.64% RMSE (Root Mean Square Error), 6.24% MAPE (Mean Absolute Percentage Error) and 2.55% MBE (Mean Bias Error), consumption time is 6.45 (s) and also the sensitivity and the accuracy ranges are 98.8% and 99%. The proposed approach is actualized using MATLAB and the realtime datasets are used for our examination.

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