Automating Vitiligo Skin Lesion Segmentation Using Convolutional Neural Networks

Makena Low, Victor Huang, Priyanka Raina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The measurement of several skin conditions' progression and severity relies on the accurate segmentation (border detection) of lesioned skin images. One such condition is vitiligo. Existing methods for vitiligo image segmentation require manual intervention, which is time and labor-intensive, as well as irreproducible between physicians. We introduce a convolutional neural network (CNN) that quickly and robustly performs such segmentations without manual intervention. We use the U-Net with a modified contracting path to generate an initial segmentation of the lesion. Then, we run the segmentation through the watershed algorithm using high-confidence pixels as 'seeds.' We train the network on 247 images with a variety of lesion sizes, complexities, and anatomical sites. Our network noticeably outperforms the state-of-the-art U-Net - scoring a Jaccard Index (JI) of 73.6% (compared to 36.7%). Segmentation occurs in a few seconds, which is a substantial improvement from the previously proposed semiautonomous watershed approach (2-29 minutes per image).

Original languageEnglish (US)
Title of host publicationISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages1992-1995
Number of pages4
ISBN (Electronic)9781538693308
DOIs
StatePublished - Apr 2020
Event17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States
Duration: Apr 3 2020Apr 7 2020

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2020-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
CountryUnited States
CityIowa City
Period4/3/204/7/20

Keywords

  • image segmentation
  • lesions
  • neural network
  • U-Net
  • vitiligo
  • watershed

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Low, M., Huang, V., & Raina, P. (2020). Automating Vitiligo Skin Lesion Segmentation Using Convolutional Neural Networks. In ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging (pp. 1992-1995). [9098682] (Proceedings - International Symposium on Biomedical Imaging; Vol. 2020-April). IEEE Computer Society. https://doi.org/10.1109/ISBI45749.2020.9098682