Paper Publications

Current position: 英文主页 > Scientific Research > Paper Publications

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

  • Release time:2025-09-03
  • Hits:

  • Impact Factor: 

    9.8

  • DOI number: 

    10.1109/TMI.2025.3597026

  • Journal: 

    IEEE Transactions on Medical Imaging

  • Key Words: 

    Image segmentation, Feature extraction, Diseases, Thickness measurement, Accuracy, Lesions, Retina, Deep learning, Data mining , Logic gates

  • Abstract: 

    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.

  • First Author: 

    Xiaoyu Zhu

  • Co-author: 

    Shiyin Li,Hongliang Bi,Lina Guan,Haiyang Liu

  • Indexed by: 

    Journal paper

  • Correspondence Author: 

    Zhaolin Lu

  • Translation or Not: 

    no

  • Date of Publication: 

    2025-08-08

  • Included Journals: 

    SCI
Address:No1,Daxue Road,Xuzhou,Jiangsu,221116,P.R.China    Email: master@cumt.edu.cn
All Rights Reserved by China University of Mining and Technology