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

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):  
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.

A Deep Adaptive Convolutional Network for Brain Tumor Segmentation from Multimodal MR Images

2019 ◽  
Author(s):  
Palash Ghosal ◽  
Shanmukha Reddy ◽  
Charan Sai ◽  
Vikas Pandey ◽  
Jayasree Chakraborty ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Convolutional Network ◽  
Brain Tumor Segmentation

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.

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