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 automati

SUBMITTER: Takahashi H 

PROVIDER: S-EPMC9381727 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature