scholarly journals Acute and sub-acute stroke lesion segmentation from multimodal MRI

2020 ◽  
Vol 194 ◽  
pp. 105521
Author(s):  
Albert Clèrigues ◽  
Sergi Valverde ◽  
Jose Bernal ◽  
Jordi Freixenet ◽  
Arnau Oliver ◽  
...  
Keyword(s):  
Acute Stroke ◽  
Lesion Segmentation ◽  
Multimodal Mri ◽  
Stroke Lesion

An active learning approach for stroke lesion segmentation on multimodal MRI data

2015 ◽  
Vol 150 ◽  
pp. 26-36 ◽  
Author(s):  
Darya Chyzhyk ◽  
Rosalía Dacosta-Aguayo ◽  
Maria Mataró ◽  
Manuel Graña
Keyword(s):  
Active Learning ◽  
Learning Approach ◽  
Lesion Segmentation ◽  
Multimodal Mri ◽  
Stroke Lesion

Stroke Lesion Segmentation and Analysis using Entropy/Otsu’s Function – A Study with Social Group Optimization

2019 ◽  
Vol 14 (4) ◽  
pp. 305-313 ◽  
Author(s):  
Suresh Chandra Satapathy ◽  
Steven Lawrence Fernandes ◽  
Hong Lin
Keyword(s):  
Social Group ◽  
Research Work ◽  
Lesion Segmentation ◽  
Permanent Disability ◽  
Brain Abnormality ◽  
Inspection Process ◽  
Stroke Lesion ◽  
Social Group Optimization ◽  
The Brain ◽  
Otsu’S Function

Background: Stroke is one of the major causes for the momentary/permanent disability in the human community. Usually, stroke will originate in the brain section because of the neurological deficit and this kind of brain abnormality can be predicted by scrutinizing the periphery of brain region. Magnetic Resonance Image (MRI) is the extensively considered imaging procedure to record the interior sections of the brain to support visual inspection process. Objective: In the proposed work, a semi-automated examination procedure is proposed to inspect the province and the severity of the stroke lesion using the MRI. associations while known disease-lncRNA associations are required only. Method: Recently discovered heuristic approach called the Social Group Optimization (SGO) algorithm is considered to pre-process the test image based on a chosen image multi-thresholding procedure. Later, a chosen segmentation procedure is considered in the post-processing section to mine the stroke lesion from the pre-processed image. Results: In this paper, the pre-processing work is executed with the well known thresholding approaches, such as Shannon’s entropy, Kapur’s entropy and Otsu’s function. Similarly, the postprocessing task is executed using most successful procedures, such as level set, active contour and watershed algorithm. Conclusion: The proposed procedure is experimentally inspected using the benchmark brain stroke database known as Ischemic Stroke Lesion Segmentation (ISLES 2015) challenge database. The results of this experimental work authenticates that, Shannon’s approach along with the LS segmentation offers superior average values compared with the other approaches considered in this research work.</P>

A Crossbred Approach for Effective Brain Stroke Lesion Segmentation

2018 ◽  
Vol 11 (1) ◽  
pp. 286-295
Author(s):  
Sunil Melingi ◽  
◽  
Vijayalakshmi Vivekanand ◽  
Keyword(s):  
Lesion Segmentation ◽  
Brain Stroke ◽  
Stroke Lesion

scholarly journals Correction: Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study

PLoS ONE ◽  
2016 ◽  
Vol 11 (2) ◽  
pp. e0149828 ◽  
Author(s):  
Oskar Maier ◽  
Christoph Schröder ◽  
Nils Daniel Forkert ◽  
Thomas Martinetz ◽  
Heinz Handels
Keyword(s):  
Ischemic Stroke ◽  
Comparison Study ◽  
Lesion Segmentation ◽  
Stroke Lesion

scholarly journals Ischemic Stroke Lesion Segmentation Using Multi-Plane Information Fusion

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 45715-45725 ◽  
Author(s):  
Long Zhang ◽  
Ruoning Song ◽  
Yuanyuan Wang ◽  
Chuang Zhu ◽  
Jun Liu ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Information Fusion ◽  
Lesion Segmentation ◽  
Stroke Lesion

scholarly journals ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

2017 ◽  
Vol 35 ◽  
pp. 250-269 ◽  
Author(s):  
Oskar Maier ◽  
Bjoern H. Menze ◽  
Janina von der Gablentz ◽  
Levin Häni ◽  
Mattias P. Heinrich ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Lesion Segmentation ◽  
Public Evaluation ◽  
Stroke Lesion ◽  
Multispectral Mri

scholarly journals Dual-Path Attention Compensation U-Net for Stroke Lesion Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Haisheng Hui ◽  
Xueying Zhang ◽  
Zelin Wu ◽  
Fenlian Li
Keyword(s):  
Input Image ◽  
Core Network ◽  
Lesion Segmentation ◽  
Coverage Area ◽  
Primary Path ◽  
Primary Network ◽  
Segmentation Methods ◽  
Mri Scans ◽  
Stroke Lesion ◽  
Segmentation Task

For the segmentation task of stroke lesions, using the attention U-Net model based on the self-attention mechanism can suppress irrelevant regions in an input image while highlighting salient features useful for specific tasks. However, when the lesion is small and the lesion contour is blurred, attention U-Net may generate wrong attention coefficient maps, leading to incorrect segmentation results. To cope with this issue, we propose a dual-path attention compensation U-Net (DPAC-UNet) network, which consists of a primary network and auxiliary path network. Both networks are attention U-Net models and identical in structure. The primary path network is the core network that performs accurate lesion segmentation and outputting of the final segmentation result. The auxiliary path network generates auxiliary attention compensation coefficients and sends them to the primary path network to compensate for and correct possible attention coefficient errors. To realize the compensation mechanism of DPAC-UNet, we propose a weighted binary cross-entropy Tversky (WBCE-Tversky) loss to train the primary path network to achieve accurate segmentation and propose another compound loss function called tolerance loss to train the auxiliary path network to generate auxiliary compensation attention coefficient maps with expanded coverage area to perform compensate operations. We conducted segmentation experiments using the 239 MRI scans of the anatomical tracings of lesions after stroke (ATLAS) dataset to evaluate the performance and effectiveness of our method. The experimental results show that the DSC score of the proposed DPAC-UNet network is 6% higher than the single-path attention U-Net. It is also higher than the existing segmentation methods of the related literature. Therefore, our method demonstrates powerful abilities in the application of stroke lesion segmentation.

scholarly journals JOURNAL CLUB: Evaluation of Diffusion Kurtosis Imaging of Stroke Lesion With Hemodynamic and Metabolic MRI in a Rodent Model of Acute Stroke

2018 ◽  
Vol 210 (4) ◽  
pp. 720-727 ◽  
Author(s):  
Dongshuang Lu ◽  
Yinghua Jiang ◽  
Yang Ji ◽  
Iris Yuwen Zhou ◽  
Emiri Mandeville ◽  
...  
Keyword(s):  
Acute Stroke ◽  
Journal Club ◽  
Rodent Model ◽  
Diffusion Kurtosis Imaging ◽  
Stroke Lesion ◽  
Diffusion Kurtosis
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