Monitoring wound healing in a 3D wound model by hyperspectral imaging and efficient clustering

Autor/in: Wahabzada, M., M. Besser, M. Khosravani, M.T. Kuska, K. Kersting, A.-K. Mahlein, E. Stürmer
Jahr: 2017
Zeitschrift: PLOS one
Seite/n: DOI 10.1371/journal.pone.0186425

Published in Projekt Veröffentlichungen, die keinem Projekt und keiner Abteilung zugeordnet sind / Abteilung Additional projects and publications

Abstract

Wound healing is a complex and dynamic process with different distinct and overlapping phases from homeostasis, inflammation and proliferation to remodelling. Monitoring the healing response of injured tissue is of high importance for basic research and clinical prac- tice. In traditional application, biological markers characterize normal and abnormal wound healing. Understanding functional relationships of these biological processes is essential for developing new treatment strategies. However, most of the present techniques (in vitro or in vivo) include invasive microscopic or analytical tissue sampling. In the present study, a non- invasive alternative for monitoring processes during wound healing is introduced. Within this context, hyperspectral imaging (HSI) is an emerging and innovative non-invasive imaging technique with different opportunities in medical applications. HSI acquires the spectral reflectance of an object, depending on its biochemical and structural characteristics. An in- vitro 3-dimensional (3-D) wound model was established and incubated without and with acute and chronic wound fluid (AWF, CWF), respectively. Hyperspectral images of each individual specimen of this 3-D wound model were assessed at day 0/5/10 in vitro, and reflectance spectra were evaluated. For analysing the complex hyperspectral data, an effi- cient unsupervised approach for clustering massive hyperspectral data was designed, based on efficient hierarchical decomposition of spectral information according to archetypal data points. It represents, to the best of our knowledge, the first application of an advanced Data Mining approach in context of non-invasive analysis of wounds using hyperspectral imagery. By this, temporal and spatial pattern of hyperspectral clusters were determined within the tissue discs and among the different treatments. Results from non-invasive imag- ing were compared to the number of cells in the various clusters, assessed by Hematoxylin/ Eosin (H/E) staining. It was possible to correlate cell quantity and spectral reflectance during wound closure in a 3-D wound model in vitro.

FaLang translation system by Faboba