Automatic computer-aided analysis of optic disc pallor in fundus photographs

被引:10
|
作者
Yang, Hee Kyung [1 ]
Oh, Ji Eun [2 ]
Han, Sang Beom [3 ]
Kim, Kwang Gi [4 ]
Hwang, Jeong-Min [1 ]
机构
[1] Seoul Natl Univ, Coll Med, Dept Ophthalmol, Bundang Hosp, 82,Gumi Ro 173 Beon Gil, Seongnam 13620, Gyeonggi Do, South Korea
[2] Natl Canc Ctr, Div Convergence Technol, Innovat Med Engn & Technol, Goyang, South Korea
[3] Kangwon Natl Univ, Kangwon Natl Univ Hosp, Dept Ophthalmol, Grad Sch Med, Chunchon, South Korea
[4] Gachon Univ, Dept Biomed Engn, Coll Med, Incheon, South Korea
基金
新加坡国家研究基金会;
关键词
automatic; computer-aided detection; optic disc; pallor; NERVE-FIBER LAYER; NEURORETINAL RIM; COLOR; ATROPHY; DIFFERENTIATION;
D O I
10.1111/aos.13970
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
Purpose Assessment of optic disc pallor in fundus photographs may be frequently misinterpreted due to the subjective nature of interpretation. We developed a fully automatic computer-aided detection (CAD) system for optic disc pallor using colour fundus photographs and evaluated the accuracy of the system. Methods A newly proposed CAD system was developed for automated segmentation and image analysis of optic disc pallor, and a logistic regression model was developed for risk analysis. A total of 230 photographs with variable degree of optic disc pallor, and 123 normal optic discs confirmed by optical coherence tomography were tested for validation of the software. Sensitivity and specificity of the CAD system in automatic detection of optic disc pallor using colour fundus photographs were evaluated. The results of manual detection of optic disc pallor on fundus photographs by two independent ophthalmologists were compared with the efficacy of the CAD system. Results The fully automated CAD system achieved a sensitivity of 95.3% and a specificity of 96.7% for detecting optic disc pallor in colour fundus images. The overall accuracy of the CAD system was 96.1%, which was superior to the results of manual detection by individual examiners. Conclusions We developed an automated CAD system that successfully detected optic disc pallor in fundus photographs. The proposed algorithm can assist the clinical judgement of ophthalmologists for detecting optic disc pallor in fundus photographs.
引用
收藏
页码:E519 / E525
页数:7
相关论文
共 50 条
  • [1] COMPUTER-AIDED DIGITIZATION OF FUNDUS PHOTOGRAPHS
    SLEIGHTHOLM, MA
    ARNOLD, J
    ALDINGTON, SJ
    KOHNER, EM
    [J]. CLINICAL PHYSICS AND PHYSIOLOGICAL MEASUREMENT, 1984, 5 (04): : 295 - 301
  • [2] Automatic Computer-Aided Diagnosis of Retinal Nerve Fiber Layer Defects Using Fundus Photographs in Optic Neuropathy
    Oh, Ji Eun
    Yang, Hee Kyung
    Kim, Kwang Gi
    Hwang, Jeong-Min
    [J]. INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2015, 56 (05) : 2872 - 2879
  • [3] Simultaneous Automatic Detection of the Optic Disc and Fovea on Fundus Photographs
    Xu, Xiayu
    Garvin, Mona K.
    Abramoff, Michael D.
    Reinhardt, Joseph M.
    [J]. MEDICAL IMAGING 2011: IMAGE PROCESSING, 2011, 7962
  • [4] Computer-aided diagnosis based on enhancement of degraded fundus photographs
    Jin, Kai
    Zhou, Mei
    Wang, Shaoze
    Lou, Lixia
    Xu, Yufeng
    Ye, Juan
    Qian, Dahong
    [J]. ACTA OPHTHALMOLOGICA, 2018, 96 (03) : E320 - E326
  • [5] Automatic Fundus Image Classification for Computer-Aided Diagonsis
    Lu, Shijian
    Liu, Jiang
    Lim, Joo Hwee
    Zhang, Zhuo
    Meng, Tan Ngan
    Wong, Wing Kee
    Li, Huiqi
    Wong, Tian Yin
    [J]. 2009 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-20, 2009, : 1453 - +
  • [6] PallorMetrics: Software for Automatically Quantifying Optic Disc Pallor in Fundus Photographs, and Associations With Peripapillary RNFL Thickness
    Gibbon, Samuel
    Muniz-Terrera, Graciela
    Yii, Fabian S. L.
    Hamid, Charlene
    Cox, Simon
    Maccormick, Ian J. C.
    Tatham, Andrew J.
    Ritchie, Craig
    Trucco, Emanuele
    Dhillon, Baljean
    MacGillivray, Thomas J.
    [J]. TRANSLATIONAL VISION SCIENCE & TECHNOLOGY, 2024, 13 (05):
  • [7] Computer-aided recognition of myopic tilted optic disc using deep learning algorithms in fundus photography
    Baek Hwan Cho
    Da Young Lee
    Kyung-Ah Park
    Sei Yeul Oh
    Jong Hak Moon
    Ga-In Lee
    Hoon Noh
    Joon Kyo Chung
    Min Chae Kang
    Myung Jin Chung
    [J]. BMC Ophthalmology, 20
  • [8] Computer-aided recognition of myopic tilted optic disc using deep learning algorithms in fundus photography
    Cho, Baek Hwan
    Lee, Young
    Park, Kyung-Ah
    Oh, Sei Yeul
    Moon, Jong Hak
    Lee, Ga-In
    Noh, Hoon
    Chung, Joon Kyo
    Kang, Min Chae
    Chung, Myung Jin
    [J]. BMC OPHTHALMOLOGY, 2020, 20 (01)
  • [9] Fully automatic localisation of the optic disc using YOLO in colour fundus photographs
    Zheng, Yalin
    Zhao, Yitian
    Chen, Xu
    Gao, Dongxu
    Bridge, Joshua
    Zhu, Wenyue
    Williams, Bryan
    [J]. INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2019, 60 (11)
  • [10] Computer-aided analysis of human fundus fluorescein angiograms
    Avakian, A
    Rambhia, A
    Elliot, KE
    Kalina, RE
    Chuang, EL
    Hwang, JN
    Parsons-Wingeter, P
    [J]. INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 1999, 40 (04) : S122 - S122