Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images

Abstract

The structure of the human vitreous varies considerably because of age-related liquefactions of the vitreous gel. These changes are poorly studied in vivo mainly because their high transparency and mobility make it difficult to obtain reliable and repeatable images of the vitreous. Optical coherence tomography can detect the boundaries between the vitreous gel and vitreous fluid, but it is difficult to obtain high resolution images that can be used to convert the images to three-dimensional (3D) images. Thus, the purpose of this study was to determine the shape and characteristics of the vitreous fluid using machine learning-based 3D modeling in which manually labelled fluid areas were used to train deep convolutional neural network (DCNN). The trained DCNN labelled vitreous fluid automatically and allowed us to obtain 3D vitreous model and to quantify the vitreous fluidic cavities. The mean volume and surface area of posterior vitreous fluidic cavities are 19.6 ± 7.8 mm3 and 104.0 ± 18.9 mm2 in eyes of 17 school children. The results suggested that vitreous fluidic cavities expanded as the cavities connects with each other, and this modeling system provided novel imaging markers for aging and eye diseases.

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“…Although OCT is routinely used to identify boundaries within the vitreous, the acquisition of high-resolution images suitable for generating 3D representations remains a challenge. A study used ML-based 3D modeling, employing a CNN network trained on manually labeled fluid areas [100]. The trained network automatically labeled vitreous fluid, generating 3D models and quantifying vitreous fluidic cavities.…”