C2MA-Net: Cross-modal Cross-Attention Network for Acute Ischemic Stroke Lesion Segmentation based on CT Perfusion Scans

2021 ◽  
pp. 1-1
Author(s):  
Tianyu Shi ◽  
Huiyan Jiang ◽  
Bin Zheng
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Ct Perfusion ◽  
Lesion Segmentation ◽  
Attention Network ◽  
Stroke Lesion

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.

scholarly journals Automatic Acute Ischemic Stroke Lesion Segmentation Using Semi-supervised Learning

2021 ◽  
Vol 14 (1) ◽  
pp. 723
Author(s):  
Bin Zhao ◽  
Shuxue Ding ◽  
Hong Wu ◽  
Guohua Liu ◽  
Chen Cao ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Supervised Learning ◽  
Lesion Segmentation ◽  
Stroke Lesion

Extra Tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences

2015 ◽  
Vol 240 ◽  
pp. 89-100 ◽  
Author(s):  
Oskar Maier ◽  
Matthias Wilms ◽  
Janina von der Gablentz ◽  
Ulrike M. Krämer ◽  
Thomas F. Münte ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Lesion Segmentation ◽  
Stroke Lesion ◽  
Mr Sequences

A Review of MRI Acute Ischemic Stroke Lesion Segmentation

2020 ◽  
Vol 12 (6) ◽  
Author(s):  
Abang Mohd Arif Anaqi Abang Isa ◽  
◽  
Kuryati Kipli ◽  
Muhammad Hamdi Mahmood ◽  
Ahmad Tirmizi Jobli ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Lesion Segmentation ◽  
Stroke Lesion
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