• ISSN 1674-8301
  • CN 32-1810/R
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Hamed Amini Amirkolaee, Hamid Amini Amirkolaee. Medical image translation using an edge-guided generative adversarial network with global-to-local feature fusion[J]. The Journal of Biomedical Research. doi: 10.7555/JBR.36.20220037
Citation: Hamed Amini Amirkolaee, Hamid Amini Amirkolaee. Medical image translation using an edge-guided generative adversarial network with global-to-local feature fusion[J]. The Journal of Biomedical Research. doi: 10.7555/JBR.36.20220037

Medical image translation using an edge-guided generative adversarial network with global-to-local feature fusion

doi: 10.7555/JBR.36.20220037
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  • Corresponding author: Hamed Amini Amirkolaee, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, N Kargar street, Tehran 1417935840, Iran. Tel/Fax: +98-930-9777140/+98-21-88008837, E-mail: hamedamini.a.k@gmail.com
  • Received: 2022-02-26
  • Revised: 2022-05-07
  • Accepted: 2022-05-13
  • Published: 2022-06-28
  • In this paper, we propose a framework based deep learning for medical image translation using paired and unpaired training data. Initially, a deep neural network with an encoder-decoder structure is proposed for image-to-image translation using paired training data. A multi-scale context aggregation approach is then used to extract various features from different levels of encoding, which are used during the corresponding network decoding stage. At this point, we further propose an edge-guided generative adversarial network for image-to-image translation based on unpaired training data. An edge constraint loss function is used to improve network performance in tissue boundaries. To analyze framework performance, we conducted five different medical image translation tasks. The assessment demonstrates that the proposed deep learning framework significant improvement beyond state-of-the-arts.

     

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