Diagnosis of patients suffering from small-fiber neuropathy is a challenging task and requires accurate measurement of the density of nerve fibers crossing the dermal-epidermal junction in the skin. Currently this is typically done by expert manual counting in microscopy images of sliced and stained skin biopsies. It is a rather subjective and labor-intensive process that would benefit greatly from more automated approaches. Previously we have explored classical image processing methods for this, with very limited success. Here we explore the potential of convolutional neural networks and deep learning for the task. The results of preliminary experiments show the networks perform close to the expert and outperform novices and our previous method.

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Keywords Bright-field microscopy, Deep learning., Nerve segmentation, Small-fiber neuropathy
Persistent URL dx.doi.org/10.1109/ISBI.2019.8759504, hdl.handle.net/1765/120565
Conference 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
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Bergwerf, H, Bechakra, M, Smal, I, Jongen, J.L.M, & Meijering, H.W. (2019). Nerve fiber segmentation in bright-field microscopy images of skin biopsies using deep learning. In Proceedings - International Symposium on Biomedical Imaging (pp. 232–235). doi:10.1109/ISBI.2019.8759504