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AuthorSalahuddin, Tooba
AuthorQidwai, Uvais
Available date2024-05-07T05:39:56Z
Publication Date2021
Publication NameProceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/IECBES48179.2021.9398843
URIhttp://hdl.handle.net/10576/54662
AbstractCorneal confocal microscopy (CCM) has been advocated as a non-invasive technique for objective diagnosis of very early neuropathy in patients by scanning the corneal subbasal nerve plexus. The obtained images provide a range of research opportunities to be explored. Current research revolves around providing automated solutions for nerve segmentation in CCM images. In this paper, we address the problem of low sensitivity of nerves in automatic segmentation caused by imbalanced pixel distribution in the CCM images. We evaluate three loss functions with varying parameters in the deep learning network, U-Net, on the images and discuss the results. We have observed that the optimal training time and convergence time for the Tversky loss function is better than the binary cross entropy and dice loss functions. This helps in getting better results faster, implying a quicker diagnosis.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectcorneal confocal microscopy
deep learning
image segmentation
medical image analysis
neuropathy
TitleEvaluation of Loss Functions for Segmentation of Corneal Nerves
TypeConference Paper
Pagination533-537
dc.accessType Abstract Only


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