Keypoint localization and parameter measurement in ultrasound biomicroscopy anterior segment images based on deep learning
BioMedical Engineering OnLine
volume 24, Article number: 53 (2025)
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Background
Accurate measurement of anterior segment parameters is crucial for diagnosing and managing ophthalmic conditions, such as glaucoma, cataracts, and refractive errors. However, traditional clinical measurement methods are often time-consuming, labor-intensive, and susceptible to inaccuracies. With the growing potential of artificial intelligence in ophthalmic diagnostics, this study aims to develop and evaluate a deep learning model capable of automatically extracting key points and precisely measuring multiple clinically significant anterior segment parameters from ultrasound biomicroscopy (UBM) images. These parameters include central corneal thickness (CCT), anterior chamber depth (ACD), pupil diameter (PD), angle-to-angle distance (ATA), sulcus-to-sulcus distance (STS), lens thickness (LT), and crystalline lens rise (CLR).
Methods
A data set of 716 UBM anterior segment images was collected from Tianjin Medical University Eye Hospital. YOLOv8 was utilized to segment four key anatomical structures: cornea–sclera, anterior chamber, pupil, and iris–ciliary body—thereby enhancing the accuracy of keypoint localization. Only images with intact posterior capsule lentis were selected to create an effective data set for parameter measurement. Ten keypoints were localized across the data set, allowing the calculation of seven essential parameters. Control experiments were conducted to evaluate the impact of segmentation on measurement accuracy, with model predictions compared against clinical gold standards.
Results
The segmentation model achieved a mean IoU of 0.8836 and mPA of 0.9795. Following segmentation, the binary classification model attained an mAP of 0.9719, with a precision of 0.9260 and a recall of 0.9615. Keypoint localization exhibited a Euclidean distance error of 58.73 ± 63.04 μm, improving from the pre-segmentation error of 71.57 ± 67.36 μm. Localization mAP was 0.9826, with a precision of 0.9699, a recall of 0.9642 and an FPS of 32.64. In addition, parameter error analysis and Bland–Altman plots demonstrated improved agreement with clinical gold standards after segmentation.
Conclusions
This deep learning approach for UBM image segmentation, keypoint localization, and parameter measurement is feasible, enhancing clinical diagnostic efficiency for anterior segment parameters.
Qinghao, M., Sheng, Z., Jun, Y. et al. Keypoint localization and parameter measurement in ultrasound biomicroscopy anterior segment images based on deep learning.
BioMed Eng OnLine 24, 53 (2025). https://doi.org/10.1186/s12938-025-01388-3
https://doi.org/10.1186/s12938-025-01388-3 bu içeriği en az 2000 kelime olacak şekilde ve alt başlıklar ve madde içermiyecek şekilde ünlü bir science magazine için İngilizce olarak yeniden yaz. Teknik açıklamalar içersin ve viral olacak şekilde İngilizce yaz. Haber dışında başka bir şey içermesin. Haber içerisinde en az 12 paragraf ve her bir paragrafta da en az 50 kelime olsun. Cevapta sadece haber olsun. Ayrıca haberi yazdıktan sonra içerikten yararlanarak aşağıdaki başlıkların bilgisi var ise haberin altında doldur. Eğer bilgi yoksa ilgili kısmı yazma.:
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Qinghao, M., Sheng, Z., Jun, Y. et al. Keypoint localization and parameter measurement in ultrasound biomicroscopy anterior segment images based on deep learning.
BioMed Eng OnLine 24, 53 (2025). https://doi.org/10.1186/s12938-025-01388-3
Image Credits: AI Generated
DOI: https://doi.org/10.1186/s12938-025-01388-3
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Tags: advancements in biomedical engineeringAI-driven imaging solutions for eye healthanterior segment parameter measurementartificial intelligence in eye careautomated measurement techniques in ophthalmologydeep learning in ophthalmic diagnosticsimproving glaucoma diagnosis with AIkeypoint localization in UBM imagesmeasuring central corneal thickness accuratelyophthalmic condition management innovationsreducing inaccuracies in clinical measurementsultrasound biomicroscopy applications
