Chinese Journal of Magnetic Resonance ›› 2025, Vol. 42 ›› Issue (2): 143-153.doi: 10.11938/cjmr20243130cstr: 32225.14.cjmr20243130

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Application of Generative Adversarial Networks Based on Global and Local Feature Information in Hippocampus Segmentation

WEI Zhihong1, KONG Xudong1, KONG Yan1, YAN Shiju2, DING Yang1, WEI Xianding1, KONG Dong1, YANG Bo1,*()   

  1. 1. Radiotherapy oncology department, Affiliated Hospital of Jiangnan University, Wuxi 214122, China
    2. School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  • Received:2024-09-03 Published:2025-06-05 Online:2024-11-18
  • Contact: *Tel: 13506177792, E-mail: wuxiyangbo@163.com.

Abstract:

Due to the complex structure and small size of the hippocampus, precise segmentation of the hippocampus remains challenging. To address this issue, this study proposes a generative adversarial network (GAN) based on global and local feature information (GLGAN) for hippocampus segmentation. First, to improve network stability and segmentation accuracy while reducing the likelihood of problems such as information loss and gradient explosion, we proposed the global GAN (GGAN) by optimizing the generator and loss function of GAN. Second, since the discriminator is essentially a binary classifier and is not sensitive to small local changes, we introduced a GAN method of dual discriminator network structure that integrates both global and local feature information. Finally, a total loss function was designed to balance GAN adversarial loss and 3D u-net segmentation loss. The experimental results show that proposed method based on GLGAN facilitates intensive evaluation of the hippocampus, and drives the discriminator to push the mask value provided by the generator to a more realistic distribution, thereby enhancing hippocampus segmentation accuracy. The Dice coefficient and IOU for hippocampus segmentation using GLGAN are 0.804 and 0.672 respectively.

Key words: generative adversarial network (GAN), 3D CNN, segmentation, hippocampus, 3D u-net

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