A Method for Retina Segmentation by Means of U-Net Network

被引:0
|
作者
Santone, Antonella [1 ]
De Vivo, Rosamaria [1 ]
Recchia, Laura [1 ]
Cesarelli, Mario [2 ]
Mercaldo, Francesco [1 ]
机构
[1] Univ Molise, Dept Med & Hlth Sci Vincenzo Tiberio, I-86100 Campobasso, Italy
[2] Univ Sannio, Dept Engn, I-82100 Benevento, Italy
关键词
retina; deep learning; segmentation; U-Net; healthcare;
D O I
10.3390/electronics13224340
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Retinal image segmentation plays a critical role in diagnosing and monitoring ophthalmic diseases such as diabetic retinopathy and age-related macular degeneration. We propose a deep learning-based approach utilizing the U-Net network for the accurate and efficient segmentation of retinal images. U-Net, a convolutional neural network widely used for its performance in medical image segmentation, is employed to segment key retinal structures, including the optic disc and blood vessels. We evaluate the proposed model on a publicly available retinal image dataset, demonstrating interesting performance in automatic retina segmentation, thus showing the effectiveness of the proposed method. Our proposal provides a promising method for automated retinal image analysis, aiding in early disease detection and personalized treatment planning.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] A Method for Polyp Segmentation Through U-Net Network
    Santone, Antonella
    Cesarelli, Mario
    Mercaldo, Francesco
    BIOENGINEERING-BASEL, 2025, 12 (03):
  • [2] Retina Blood Vessels Semantic Segmentation Method Based on Modified U-Net
    Luo, Ling
    Chen, Dali
    Xue, Dingyu
    PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC), 2018, : 1892 - 1895
  • [3] The Augmentation Data of Retina Image for Blood Vessel Segmentation Using U-Net Convolutional Neural Network Method
    Erwin
    Safmi, Asri
    Desiani, Anita
    Suprihatin, Bambang
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2022, 21 (01)
  • [4] Improved U-NET Semantic Segmentation Network
    Gao, Xueyan
    Fang, Lijin
    PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE, 2020, : 7090 - 7095
  • [5] Improved U-Net Network Segmentation Method for Remote Sensing Image
    Zhong, Letian
    Lin, Yong
    Sul, Yian
    Fang, Xianbao
    2022 IEEE 6TH ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC), 2022, : 1034 - 1039
  • [6] Retinal Vessel Segmentation Method Based on Improved U-NET Network
    Chang, Longdan
    Ren, Kan
    Wan, Minjie
    Chen, Qian
    AOPC 2021: NOVEL TECHNOLOGIES AND INSTRUMENTS FOR ASTRONOMICAL MULTI-BAND OBSERVATIONS, 2021, 12069
  • [7] GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation
    Li, Yunxiang
    Wang, Shuai
    Wang, Jun
    Zeng, Guodong
    Liu, Wenjun
    Zhang, Qianni
    Jin, Qun
    Wang, Yaqi
    MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2021, 2021, 12966 : 386 - 395
  • [8] Semantic Segmentation of Lung Tissues in HRCT Images by Means of a U-Net Convolutional Network
    Hernandez-Juarez, Sarahi
    Mejia-Rodriguez, Aldo R.
    Arce-Santana, Edgar R.
    Charleston-Villalobos, S.
    Aljama-Corrales, A. T.
    Gonzalez-Camarena, R.
    Mejia-Avila, M.
    VIII LATIN AMERICAN CONFERENCE ON BIOMEDICAL ENGINEERING AND XLII NATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING, 2020, 75 : 426 - 434
  • [9] Attention residual convolution neural network based on U-net (AttentionResU-Net) for retina vessel segmentation
    Zhao, Shun
    Liu, Tao
    Liu, Bowen
    Ruan, Kun
    2019 5TH INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND MATERIAL APPLICATION, 2020, 440
  • [10] Stone segmentation based on improved U-Net network
    Chen, Ning
    Ma, Xinkai
    Luo, Haixia
    Peng, Jun
    Jin, Shangzhu
    Wu, Xiao
    Zhou, Yongsheng
    SIGNAL IMAGE AND VIDEO PROCESSING, 2024, 18 (SUPPL 1) : 895 - 908