Cornea · 2021
New Freeware for Image Analysis of Lissamine Green Conjunctival Staining.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Courrier Emilie, Renault Didier, Dib Elian, Urrea Caroline, Kaspi Mathilde, Fournier Corinne, Lépine Thierry, Chiambaretta Frédéric, Hor Guillaume, He Zhiguo, Garcin Thibaud, Thuret Gilles, Gain Philippe
Аннотация
PURPOSE: Lissamine green (LG) is often used in addition to fluorescein to assess the severity of conjunctival damage in dry eye syndrome, which is graded manually. Our purpose was to describe an algorithm designed for image analysis of LG conjunctival staining.
METHODS: Twenty pictures of patients suffering from dry eye with visible LG conjunctival staining were selected. The images were taken by 2 different digital slit lamps with a white light source and a red filter transmitting over the wavelengths absorbed by LG. Conjunctival staining appeared in black on a red background. The red channel was extracted from the original image. Stained areas were then detected using a Laplacian of Gaussian filter and applying a threshold whose value was determined manually on a subset of images. The same algorithm parameters remained constant thereafter. LG-stained areas were also drawn manually by 2 experts as a reference.
RESULTS: The delineation obtained by the algorithm closely matched the actual contours of the punctate dots. In 19 cases of 20 (95%), the algorithm found the same Oxford grade as the experts, even for confluent staining that was detected as a multitude of dots by the algorithm but not by the experts, resulting in a high overestimation of the total number of dots (without mismatching the Oxford grade estimated by the experts). The results were similar for the 2 slit-lamp imaging systems.
CONCLUSIONS: This efficient new image-analysis algorithm yields results consistent with subjective grading and may offer advantages of automation and scalability in clinical trials.
METHODS: Twenty pictures of patients suffering from dry eye with visible LG conjunctival staining were selected. The images were taken by 2 different digital slit lamps with a white light source and a red filter transmitting over the wavelengths absorbed by LG. Conjunctival staining appeared in black on a red background. The red channel was extracted from the original image. Stained areas were then detected using a Laplacian of Gaussian filter and applying a threshold whose value was determined manually on a subset of images. The same algorithm parameters remained constant thereafter. LG-stained areas were also drawn manually by 2 experts as a reference.
RESULTS: The delineation obtained by the algorithm closely matched the actual contours of the punctate dots. In 19 cases of 20 (95%), the algorithm found the same Oxford grade as the experts, even for confluent staining that was detected as a multitude of dots by the algorithm but not by the experts, resulting in a high overestimation of the total number of dots (without mismatching the Oxford grade estimated by the experts). The results were similar for the 2 slit-lamp imaging systems.
CONCLUSIONS: This efficient new image-analysis algorithm yields results consistent with subjective grading and may offer advantages of automation and scalability in clinical trials.