Abstract
Clinical parameters related to the corneal endothelium can only be estimated by segmenting endothelial cell images. Specular microscopy is the current standard technique to image the endothelium, but its low SNR make the segmentation a complicated task. Recently, we proposed a method to segment such images by starting with an oversegmented image and merging the superpixels that constitute a cell. Here, we show how our merging method provides better results than optimizing the segmentation itself. Furthermore, our method can provide accurate results despite the degree of the initial oversegmentation, resulting into a precision and recall of 0.91 for the optimal oversegmentation.
This work was supported by ZonMw under Grants 842005004 and 842005007.
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Vigueras-Guillén, J.P., Andrinopoulou, E.R., Engel, A., Lemij, H.G., van Rooij, J., Vermeer, K.A., van Vliet, L.J.: Corneal endothelial cell segmentation by classifier-based merging of oversegmented images (2018, submitted)
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Vigueras-Guillén, J.P., Engel, A., Lemij, H.G., van Rooij, J., Vermeer, K.A., van Vliet, L.J. (2018). Improved Accuracy and Robustness of a Corneal Endothelial Cell Segmentation Method Based on Merging Superpixels. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_72
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