Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers

2014 ◽  
Author(s):  
Oskar Maier ◽  
Matthias Wilms ◽  
Janina von der Gablentz ◽  
Ulrike Krämer ◽  
Heinz Handels
Keyword(s):  
Support Vector Machine ◽  
Ischemic Stroke ◽  
Support Vector ◽  
Lesion Segmentation ◽  
Mr Images ◽  
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 Brain SegNet: 3D Local Refinement Network for Brain Lesion Segmentation

2020 ◽  
Author(s):  
Xiaojun Hu ◽  
Weijian Luo ◽  
Jiliang Hu ◽  
Sheng Guo ◽  
Weilin Huang ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Brain Tumor ◽  
Surgical Planning ◽  
Brain Lesion ◽  
Brain Lesions ◽  
Tumor Segmentation ◽  
Lesion Segmentation ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Stroke Lesion

Abstract MR images (MRIs) accurate segmentation of brain lesions is important for improving cancer diagnosis, surgical planning, and prediction of outcome. However, manual and accurate segmentation of brain lesions from 3D MRIs is highly expensive, time-consuming, and prone to user biases. We present an efficient yet conceptually simple brain segmentation network (referred as Brain SegNet), which is a 3D residual framework for automatic voxel-wise segmentation of brain lesion. Our model is able to directly predict dense voxel segmentation of brain tumor or ischemic stroke regions in 3D brain MRIs. The proposed 3D segmentation network can run at about 0.5s per MRIs - about 50 times faster than previous approaches [1,2]. Our model is evaluated on the BRATS 2015 benchmark for brain tumor segmentation, where it obtains state-of-the-art results, by surpassing recently published results reported in [1,2]. We further applied the proposed Brain SegNet for ischemic stroke lesion outcome prediction, with impressive results achieved on the Ischemic Stroke Lesion Segmentation (ISLES) 2017 database.

scholarly journals Brain SegNet: 3D Local Refinement Network for Brain Lesion Segmentation

2019 ◽  
Author(s):  
Xiaojun Hu ◽  
Weijian Luo ◽  
Jiliang Hu ◽  
Sheng Guo ◽  
Weilin Huang ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Brain Tumor ◽  
Surgical Planning ◽  
Brain Lesion ◽  
Brain Lesions ◽  
Tumor Segmentation ◽  
Lesion Segmentation ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Stroke Lesion

Abstract MR images (MRI) accurate segmentation of brain lesions is important for improving cancer diagnosis, surgical planning, and prediction of outcome. However, manual and accurate segmentation of brain lesions from 3D MRIs is highly expensive, time-consuming, and prone to user biases. We present an efficient yet conceptually simple brain segmentation network (referred as Brain SegNet), which is a 3D residual framework for automatic voxel-wise segmentation of brain lesion. Our model is able to directly predict dense voxel segmentation of brain tumor or ischemic stroke regions in 3D brain MRIs. The proposed 3D segmentation network can run at about 0.5s per MRIs - about 50 times faster than previous approaches [1,2]. Our model is evaluated on the BRATS 2015 benchmark for brain tumor segmentation, where it obtains state-of-the-art results, by surpassing recently published results reported in [1,2]. We further applied the proposed Brain SegNet for ischemic stroke lesion outcome prediction, with impressive results achieved on the Ischemic Stroke Lesion Segmentation (ISLES) 2017 database.

scholarly journals Brain SegNet: 3D Local Refinement Network for Brain Lesion Segmentation

2019 ◽  
Author(s):  
Weijian Luo ◽  
Xiaojun Hu ◽  
Jiliang Hu ◽  
Sheng Guo ◽  
Weilin Huang ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Brain Tumor ◽  
Surgical Planning ◽  
Brain Lesion ◽  
Brain Lesions ◽  
Tumor Segmentation ◽  
Lesion Segmentation ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Stroke Lesion

Abstract MR images (MRIs) accurate segmentation of brain lesions is important for improving cancer diagnosis, surgical planning, and prediction of outcome. However, manual and accurate segmentation of brain lesions from 3D MRIs is highlyexpensive, time-consuming, and prone to user biases. We present an efficient yet conceptually simple brain segmentation network (referred as Brain SegNet), which is a 3D residual framework for automatic voxel-wise segmentation of brain lesion. Our model is able to directly predict dense voxel segmentation of brain tumor or ischemic stroke regions in 3D brain MRIs. The proposed 3D segmentation network can run at about 0.5s per MRIs - about 50 times faster than previous approaches [1,2]. Our model is evaluated on the BRATS 2015 benchmark for brain tumor segmentation, where it obtains state-of-the-art results, by surpassing recently published results reported in [1,2]. We further applied the proposed Brain SegNet for ischemic stroke lesion outcome prediction, with impressive results achieved on the Ischemic Stroke Lesion Segmentation (ISLES) 2017 database.

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
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