• Subscribe

Image of the week - Melanoma image analysis

Image of the week - Melanoma image analysis


Nancy Thomas, an associate professor in the School of Medicine at the University of North Carolina specializing in melanoma research, leads the Melanoma Image Analysis project. Her team is looking at the use of image analysis approaches to aid in diagnosis and prognosis of melanoma from patient pathology slides. Based on prior research from Thomas and others, we know that observable characteristics of melanoma can stem from more than one type of genetic mutation.

This project is working under the hypothesis that image analysis will allow for the quantitative extraction of melanoma features and that such features can be used to both identify cancerous tissues and link melanoma phenotype with genotype. If true, this will lead to enhanced prediction of patient survival and guide future treatment options. The research should result in a more robust and clinically relevant melanoma classification system based on causal pathways.

Computing experts at the Renaissance Computing Institute (RENCI) work with the project team to develop the image analysis algorithms for extracting melanoma features that can lead to clinically relevant tests.

Image courtesy of RENCI

Join the conversation

Do you have story ideas or something to contribute? Let us know!

Copyright © 2019 Science Node ™  |  Privacy Notice  |  Sitemap

Disclaimer: While Science Node ™ does its best to provide complete and up-to-date information, it does not warrant that the information is error-free and disclaims all liability with respect to results from the use of the information.

Republish

We encourage you to republish this article online and in print, it’s free under our creative commons attribution license, but please follow some simple guidelines:
  1. You have to credit our authors.
  2. You have to credit ScienceNode.org — where possible include our logo with a link back to the original article.
  3. You can simply run the first few lines of the article and then add: “Read the full article on ScienceNode.org” containing a link back to the original article.
  4. The easiest way to get the article on your site is to embed the code below.