Corneal endothelium assessment is carried out via specular microscopy imaging. However, automated image analysis often fails due to inadequate image quality conditions or the presence of dark regions in pathologies such as Fuchs’ dystrophy. Therefore, an early reliable image classification strategy is required before automated evaluation based on cell segmentation. Moreover, conventional classification approaches rely on manually labeled data which are difficult to obtain. We propose a two-stage semi-supervised classification algorithm, feature detection and prediction of a blurring level and guttae severity that allows us to cluster images based on the degree of segmentation complexity. For validation, we developed a web-based annotation application and surveyed a pair of expert ophthalmologists for grading a portion of the 1169 images. Preliminary results show that this approach provides a reliable and fast approach for corneal endothelial cell (CEC) image classification.
Automated cell counting in in-vivo specular microscopy images is challenging, especially when single-cell segmentation methods fail due to corneal dystrophy. We aim to obtain reliable cell segmentation from specular microscopy images of both healthy and pathological corneas. Here, we cast the problem of cell segmentation as a supervised multi-class classification problem. Hence, the goal is to learn a mapping relation between an input specular microscopy image and its labeled counterpart, identifying healthy (cells) and dysfunctional regions (e.g., guttae). Using a generative adversarial approach, we trained a U-net model by extracting 96×96 pixel patches from corneal endothelial cell images and the corresponding manual segmentation by a group of expert physicians. Preliminary results show the method's potential to deliver reliable feature segmentation, enabling more accurate cell density estimations for assessing the cornea's state.
Automated cell counting in in-vivo specular microscopy images is challenging, especially in situations where single-cell segmentation methods fail due to pathological conditions. This work aims to obtain reliable cell segmentation from specular microscopy images of both healthy and pathological corneas. We cast the problem of cell segmentation as a supervised multi-class segmentation problem. The goal is to learn a mapping relation between an input specular microscopy image and its labeled counterpart, indicating healthy (cells) and pathological regions (e.g., guttae). We trained a U-net model by extracting 96×96 pixel patches from corneal endothelial cell images and the corresponding manual segmentation by a physician. Encouraging results show that the proposed method can deliver reliable feature segmentation enabling more accurate cell density estimations for assessing the state of the cornea.
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