Ophthalmic research · 2020
Automatic Classification of Anterior Chamber Angle Based on Ultrasound Biomicroscopy Images.
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Yu Jian, Li Wanyue, Chen Qian, Deng Guohua, Jiang Chunhui, Liu Guangxing, Shi Guohua, Sun Xinghuai
Аннотация
INTRODUCTION: Evaluating the anterior chamber angle (ACA) is important for the early diagnosis and treatment of primary angle-closure glaucoma. The assessment of ultrasound biomicroscopy (UBM) images usually requires well-trained ophthalmologists and screening for patients with narrow ACA is usually time- and labor-intensive. Therefore, the automatic assessment of UBM could be cost-effective and valuable in daily practice.
OBJECTIVE: The objective of this study is to develop an automatic method for localizing and classifying ACA based on UBM images.
METHODS: UBM images were collected and a coarse-to-fine method was used to localize the apex of the angle recess. By analyzing the grayscale features around the angle recess, closed angles were identified, and the rest were then classified as open or narrow angles, based on the degree of ACA. Using manual classification as the reference standard, the overall accuracy (OAcc), sensitivity (Sen), specificity (Spe), and balanced accuracy of the automatic classification method were evaluated.
RESULTS: A total of 540 UBM images from 290 participants were analyzed. Using these UBM images and the proposed method, the ACA was classified as open, narrow, or closed. During processing, the method localized the angle recess with 95% accuracy. The OAcc of the ACA classification was 77.8%, and the Spe and Sen of our method were 85.8 and 81.7% for angle closure; 88.9 and 75.6% for open angles; 91.9 and 76.1% for narrow angles, respectively.
CONCLUSIONS: Our method of automatic angle localization and classification based on UBM images is feasible and reliable. The automatic classification of ACA provides a basis and reference for future studies.
OBJECTIVE: The objective of this study is to develop an automatic method for localizing and classifying ACA based on UBM images.
METHODS: UBM images were collected and a coarse-to-fine method was used to localize the apex of the angle recess. By analyzing the grayscale features around the angle recess, closed angles were identified, and the rest were then classified as open or narrow angles, based on the degree of ACA. Using manual classification as the reference standard, the overall accuracy (OAcc), sensitivity (Sen), specificity (Spe), and balanced accuracy of the automatic classification method were evaluated.
RESULTS: A total of 540 UBM images from 290 participants were analyzed. Using these UBM images and the proposed method, the ACA was classified as open, narrow, or closed. During processing, the method localized the angle recess with 95% accuracy. The OAcc of the ACA classification was 77.8%, and the Spe and Sen of our method were 85.8 and 81.7% for angle closure; 88.9 and 75.6% for open angles; 91.9 and 76.1% for narrow angles, respectively.
CONCLUSIONS: Our method of automatic angle localization and classification based on UBM images is feasible and reliable. The automatic classification of ACA provides a basis and reference for future studies.