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Automatic Choroid Segmentation and Thickness Measurement Based on Mixed Attention-guided Multiscale Feature Fusion Network

发布时间：2025-09-03点击次数：

  • 影响因子：9.8
  • DOI码：10.1109/TMI.2025.3597026
  • 发表刊物：IEEE Transactions on Medical Imaging
  • 关键字：Image segmentation, Feature extraction, Diseases, Thickness measurement, Accuracy, Lesions, Retina, Deep learning, Data mining , Logic gates
  • 摘要：Choroidal thickness variations serve as critical biomarkers for numerous ophthalmic diseases. Accurate segmentation and quantification of the choroid in optical coherence tomography (OCT) images is essential for clinical diagnosis and disease progression monitoring. Due to the small number of disease types in the public OCT dataset involving changes in choroidal thickness and the lack of a publicly available labeled dataset, we constructed the Xuzhou Municipal Hospital (XZMH)-Choroid dataset. This dataset contains annotated OCT images of normal and eight choroid-related diseases. However, segmentation of the choroid in OCT images remains a formidable challenge due to the confounding factors of blurred boundaries, non-uniform texture, and lesions. To overcome these challenges, we proposed a mixed attention-guided multiscale feature fusion network (MAMFF-Net). This network integrates a Mixed Attention Encoder (MAE) for enhanced fine-grained feature extraction, a deformable multiscale feature fusion path (DMFFP) for adaptive feature integration across lesion deformations, and a multiscale pyramid layer aggregation (MPLA) module for improved contextual representation learning. Through comparative experiments with other deep learning methods, we found that the MAMFF-Net model has better segmentation performance than other deep learning methods (mDice: 97.44, mIoU: 95.11, mAcc: 97.71). Based on the choroidal segmentation implemented in MAMFF-Net, an algorithm for automated choroidal thickness measurement was developed, and the automated measurement results approached the level of senior specialists.
  • 第一作者：朱小玉
  • 合写作者：李世银,毕红亮,Lina Guan,刘海洋
  • 论文类型：期刊论文
  • 通讯作者：卢兆林
  • 是否译文：否
  • 发表时间：2025-08-08
  • 收录刊物：SCI

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毕红亮

个人信息

  • 副教授
    硕士生导师
  • 教师拼音名称：bihongliang
  • 所在单位：信息与控制工程学院
  • 性别：男
  • 联系方式：bihongliang@cumt.edu.cn
  • 学位：工学博士学位
  • 职称：副教授

学术荣誉：

  • 2022当选：江苏双创人才

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