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Deep learning based vein segmentation from susceptibility-weighted images

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Abstract

Susceptibility-weighted images (SWIs) have recently been confirmed to be more sensitive to acute ischemic stroke than diffusion weighted images in the form of the presence of small veins shortly after the symptom onset. Accurate segmentation of small veins in SWIs is critical for quantitative diagnosis, individual therapy and outcome prediction of acute ischemic stroke. It is challenging to segment veins in SWIs as they exhibit substantial variability and intensity inhomogeneity within a patient and among patients, which may even be hard for experts to delineate manually. A deep convolutional neural network is proposed to segment veins in SWIs with two main contributions: dense connection to concatenate feature maps from preceding layers to enhance network performance, and a hybrid loss function comprising of classification accuracy and global region overlap terms to handle class imbalance. Experiments have been conducted on 10 consecutive patients with acute ischemic stroke using leave-one-out validation, yielding the best Dice coefficient (0.756 ± 0.043) (p < 0.001) as compared with 3 relevant methods. The proposed method could provide a potential tool to quantify veins in SWIs with accuracy to assist decision making especially for thrombolytic therapy.

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Acknowledgements

This work has been supported by: National Key R&D Program of China 2018YFB1105600, National Natural Science Foundation of China 61671440, Shenzhen Peacock Technology Innovation Project KQJSCX20170731162830878, National Natural Science Foundation of China 61672510, and Shenzhen Science and Technology Program JCYJ20160331191401141. The authors wish to thank Dr. Ahmed Elazab from Shenzhen University for his help in enhancing the language usage.

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Authors and Affiliations

  1. Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, University Town of Shenzhen, Shenzhen, 518055, People’s Republic of China

    Xiaodong Zhang & Qingmao Hu

  2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055, China

    Xiaodong Zhang & Qingmao Hu

  3. Department of Vascular Neurosurgery, New Era Stroke Care and Research Institute, PLA Rocket Force General Hospital, Beijing, 100088, China

    Yiqun Zhang

  4. CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China

    Qingmao Hu

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  1. Xiaodong Zhang
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  2. Yiqun Zhang
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Correspondence to Qingmao Hu.

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Zhang, X., Zhang, Y. & Hu, Q. Deep learning based vein segmentation from susceptibility-weighted images. Computing 101, 637–652 (2019). https://doi.org/10.1007/s00607-018-0677-7

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  • DOI: https://doi.org/10.1007/s00607-018-0677-7

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