Automatic Segmentation of Ischemic Stroke Lesions in CT Perfusion Maps Using Deep Learning Networks
Stroke is the third leading cause of death and the largest cause of acquired disability worldwide. Classification of stroke lesions is vital in recovery, diagnosis, outcome assessment, and treatment planning. The current standard approach for segmenting ischemic stroke lesions is based on thresholdi...
Saved in:
Main Authors: | Lida Zare Lahijan, Saeed Meshgini, Reza Afrouzian |
---|---|
Format: | Article |
Language: | English |
Published: |
University of Isfahan
2024-09-01
|
Series: | هوش محاسباتی در مهندسی برق |
Subjects: | |
Online Access: | https://isee.ui.ac.ir/article_29109_4c9dae37a178345e520cd93facbff339.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks
by: A.V. Dobshik, et al.
Published: (2023-10-01) -
Ischemic Stroke Lesion Segmentation on Multiparametric CT Perfusion Maps Using Deep Neural Network
by: Ankit Kandpal, et al.
Published: (2025-01-01) -
Evaluation of Siemens Healthineers’ StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke
by: Lynnet-Samuel J. Teichmann, et al.
Published: (2025-01-01) -
Serum uric acid/creatinine ratio and 1-year stroke recurrence in patient with acute ischemic stroke and abnormal renal function: results from the Xi'an stroke registry study of China
by: Zhongzhong Liu, et al.
Published: (2025-02-01) -
Contrast quality control for segmentation task based on deep learning models—Application to stroke lesion in CT imaging
by: Juliette Moreau, et al.
Published: (2025-02-01)