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

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 practice. 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 efficient 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 imaging 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

Comparison for Kmeans and XHC for different number of clusters.

<p>With increasing number of clusters, Kmeans resulted in more clusters around the wound as this area represented the major part of the tissue. XHC further highlighted the middle part of the images, which was the result of a hierarchical decomposition of the signatures. This allowed a better investigation and comparison of the wound tissue and healing progress.</p

Automated and efficient interpretation of 3D wound models by non-invasive hyperspectral imaging <i>in vitro</i>.

<p>Experimental design and work flow showing the different steps for monitoring wound healing from hyperspectral imaging data to interpretation using an efficient approach for unsupervised classification of wound tissue based on hierarchical decomposition according to archetypal data points.</p

Representative spectra of 3-dimensional wound models.

<p>(a) Cluster representatives after computing of XHC for the hyperspectral image data for. The number of cluster was set to <i>k</i> = 7 that could reflect biological processes in this cell culture system. The corresponding signatures represented different regions of the tissues. The dotted line was produced by a part of the tissue covered with fluid resulting in overexposure during the measurement. (b-c) The quantification of pixel densities per cluster. The y-axis is shown in log scale. AWF and CWF induced no significant differences in pixel densities compared to the control situation, however, the dark blue cluster was absent after 10 days <i>in vitro</i> without any supplements.</p

Histological classification of hyperspectral cluster means in relation to wound healing processes.

<p>(a) H/E staining was performed to monitor the wound healing progress morphologically over a specific time period <i>in vitro</i>. The different stages were presented as false color zoomed images of the hyperspectral clusters for the wounds. (b) The quantification of the cell number in different regions of interests revealed the first time a correlation between spectral reflectance and cell quantity in the tissue. (c) Immunohistochemical investigation of CXCR4, a marker for migratory cells, determined no correlations to reflectance data. Additionally, the cells generating the characteristic hyperspectral signature did not correspond to Caspase 3-expressing apoptotic cells (not shown). Scale bar: 250 <i>μ</i>m.</p