TY - JOUR AU - Vigueras-Guillén, Juan P. AU - Sari, Busra AU - Goes, Stanley F. AU - Lemij, Hans G. AU - van Rooij, Jeroen AU - Vermeer, Koenraad A. AU - van Vliet, Lucas J. PY - 2019 DA - 2019/01/30 TI - Fully convolutional architecture vs sliding-window CNN for corneal endothelium cell segmentation JO - BMC Biomedical Engineering SP - 4 VL - 1 IS - 1 AB - Corneal endothelium (CE) images provide valuable clinical information regarding the health state of the cornea. Computation of the clinical morphometric parameters requires the segmentation of endothelial cell images. Current techniques to image the endothelium in vivo deliver low quality images, which makes automatic segmentation a complicated task. Here, we present two convolutional neural networks (CNN) to segment CE images: a global fully convolutional approach based on U-net, and a local sliding-window network (SW-net). We propose to use probabilistic labels instead of binary, we evaluate a preprocessing method to enhance the contrast of images, and we introduce a postprocessing method based on Fourier analysis and watershed to convert the CNN output images into the final cell segmentation. Both methods are applied to 50 images acquired with an SP-1P Topcon specular microscope. Estimates are compared against a manual delineation made by a trained observer. SN - 2524-4426 UR - https://doi.org/10.1186/s42490-019-0003-2 DO - 10.1186/s42490-019-0003-2 ID - Vigueras-Guillén2019 ER -