Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks

Author:

Wang GuotaiORCID,Song Tao,Dong Qiang,Cui Mei,Huang Ning,Zhang Shaoting

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference60 articles.

1. 3D U-Net: learning dense volumetric segmentation from sparse annotation;Abdulkadir,2016

2. Ischemic stroke lesion segmentation in CT perfusion scans using pyramid pooling and focal loss;Abulnaga,2018

3. Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., Asari, V. K., 2018. Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation. arXiv:1802.06955.

4. Fully automatic segmentation for ischemic stroke using CT perfusion maps;Anand,2018

5. Reconstruction of 7T-like images from 3T MRI;Bahrami;IEEE Trans. Med. Imaging,2016

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