scholarly journals Significance of99mTc-ECD SPECT in Acute and Subacute Ischemic Stroke: Comparison with MR Images Including Diffusion and Perfusion Weighted Images

2002 ◽  
Vol 43 (2) ◽  
pp. 211 ◽  
Author(s):  
Hyun Sook Kim ◽  
Dong Ik Kim ◽  
Jong Doo Lee ◽  
Eun Kee Jeong ◽  
Tae Sub Chung ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Mr Images

Outcomes of intra-arterial thrombolytic treatment in acute ischemic stroke patients with a matched defect on diffusion and perfusion MR images

2011 ◽  
Vol 4 (2) ◽  
pp. 105-109 ◽  
Author(s):  
Gurpreet S Sandhu ◽  
Pankit T Parikh ◽  
Daniel P Hsu ◽  
Kristine A Blackham ◽  
Robert W Tarr ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Thrombolytic Treatment ◽  
Mr Images ◽  
Stroke Patients ◽  
Perfusion Mr

Evaluation of Ischemic Stroke Region From CT/MR Images Using Hybrid Image Processing Techniques

2018 ◽  
pp. 194-219 ◽  
Author(s):  
Rajinikanth V. ◽  
Suresh Chandra Satapathy ◽  
Nilanjan Dey ◽  
Hong Lin
Keyword(s):  
Image Processing ◽  
Ischemic Stroke ◽  
Hybrid Approach ◽  
Rapid Onset ◽  
Processing Technique ◽  
Experimental Result ◽  
Mr Images ◽  
Brain Abnormality ◽  
Social Group Optimization ◽  
The Brain

An ischemic stroke (IS) naturally originates with rapid onset neurological shortfall, which can be verified by analyzing the internal regions of brain. Computed tomography (CT) and magnetic resonance image (MRI) are the commonly used non-invasive medical examination techniques used to record the brain abnormalities for clinical study. In order to have a pre-opinion regarding the brain abnormality in clinical level, it is essential to use a suitable image processing tool to appraise the digital CT/MR images. In this chapter, a hybrid image processing technique based on the social group optimization assisted Tsallis entropy and watershed segmentation (WS) is proposed to examine ischemic stroke region from digital CT/MR images. For the experimental study, the digital CT/MRI datasets like Radiopedia, BRATS-2013, and ISLES-2015 are considered. Experimental result of this study confirms that, proposed hybrid approach offers superior results on the considered image datasets.

Analysis of Ischemic Stroke MR Images by Means of Brain Atlases of Anatomy and Blood Supply Territories

2006 ◽  
Vol 13 (8) ◽  
pp. 1025-1034 ◽  
Author(s):  
Wieslaw L. Nowinski ◽  
Guoyu Qian ◽  
Bhanu Prakash Kirgaval Nagaraja ◽  
Arumugam Thirunavuukarasuu ◽  
Qingmao Hu ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Blood Supply ◽  
Mr Images ◽  
Brain Atlases

Reduced rCBV Ratio in Perfusion-Weighted MR Images Predicts Poor Outcome after Thrombolysis in Acute Ischemic Stroke

2011 ◽  
Vol 65 (5) ◽  
pp. 257-263 ◽  
Author(s):  
Hyang-I Park ◽  
Jae-Kwan Cha ◽  
Myung-Jin Kang ◽  
Dae-Hyun Kim ◽  
Nam-Tae Yoo ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Mr Images ◽  
Poor Outcome

scholarly journals Deep Learning-Based Acute Ischemic Stroke Lesion Segmentation Method on Multimodal MR Images Using a Few Fully Labeled Subjects

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bin Zhao ◽  
Zhiyang Liu ◽  
Guohua Liu ◽  
Chen Cao ◽  
Song Jin ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Segmentation Method ◽  
Lesion Segmentation ◽  
Resonance Imaging ◽  
Mr Images ◽  
Dice Coefficient ◽  
Automatic Methods ◽  
Stroke Lesion ◽  
Magnetic Resonance Imaging Mri

Acute ischemic stroke (AIS) has been a common threat to human health and may lead to severe outcomes without proper and prompt treatment. To precisely diagnose AIS, it is of paramount importance to quantitatively evaluate the AIS lesions. By adopting a convolutional neural network (CNN), many automatic methods for ischemic stroke lesion segmentation on magnetic resonance imaging (MRI) have been proposed. However, most CNN-based methods should be trained on a large amount of fully labeled subjects, and the label annotation is a labor-intensive and time-consuming task. Therefore, in this paper, we propose to use a mixture of many weakly labeled and a few fully labeled subjects to relieve the thirst of fully labeled subjects. In particular, a multifeature map fusion network (MFMF-Network) with two branches is proposed, where hundreds of weakly labeled subjects are used to train the classification branch, and several fully labeled subjects are adopted to tune the segmentation branch. By training on 398 weakly labeled and 5 fully labeled subjects, the proposed method is able to achieve a mean dice coefficient of 0.699 ± 0.128 on a test set with 179 subjects. The lesion-wise and subject-wise metrics are also evaluated, where a lesion-wise F1 score of 0.886 and a subject-wise detection rate of 1 are achieved.

A Hybrid Approach for Sub-Acute Ischemic Stroke Lesion Segmentation Using Random Decision Forest and Gravitational Search Algorithm

2019 ◽  
Vol 15 (2) ◽  
pp. 170-183
Author(s):  
Sunil Babu Melingi ◽  
V. Vijayalakshmi
Keyword(s):  
Ischemic Stroke ◽  
Magnetic Resonance ◽  
Acute Ischemic Stroke ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Mr Images ◽  
Random Decision Forest ◽  
Number Of Leaves ◽  
Gravitational Search ◽  
Decision Forest

Background: The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. We evaluate the impact of segmentation technique during the time of breaking down the capacities of the cerebrum. </P><P> Objective: The main objective of this paper is to segment the ischemic stroke lesions in Magnetic Resonance (MR) images in the presence of other pathologies like neurological disorder, encephalopathy, brain damage, Multiple sclerosis (MS). Methods: In this paper, we utilize a hybrid way to deal with segment the ischemic stroke from alternate pathologies in magnetic resonance (MR) images utilizing Random Decision Forest (RDF) and Gravitational Search Algorithm (GSA). The RDF approach is an effective machine learning approach. Results: The RDF strategy joins two parameters; they are; the number of trees in the forest and the number of leaves per tree; it runs quickly and proficiently when dealing with vast data. The GSA algorithm is utilized to optimize the RDF data for choosing the best number of trees and the number of leaves per tree in the forest. Conclusion: This paper provides a new hybrid GSA-RDF classifier technique to segment the ischemic stroke lesions in MR images. The experimental results demonstrate that the proposed technique has the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Error (MBE) ranges are 16.5485 %, 7.2654 %, and 2.4585 %individually. The proposed RDF-GSA algorithm has better precision and execution when compared with the existing ischemic stroke segmentation method.

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