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.

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE
Pages232-235
Volume2019-April
ISBN (Electronic)9781538636411
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 8 Apr 201911 Apr 2019

Conference

Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
CountryItaly
CityVenice
Period8/04/1911/04/19

    Research areas

  • Bright-field microscopy, Deep learning., Nerve segmentation, Small-fiber neuropathy

ID: 71045122