Non-invasive automated wound analysis via deep learning neural networks - Project Summary:
Each year millions of Americans develop chronic wounds, which require advanced wound care that has been
estimated to cost $50 Billion annually. However, our understanding of chronic wounds and how to treat them
has been limited by a lack of established methods to objectively characterize and measure wound features.
Detailed assessments of wounds in the clinic and research laboratory often occur through histological analysis
of tissue biopsies. This information can provide insight into cellular migration into the wound, cellular proliferation
at the edge of the wound, infection, and fibrosis. However, the collection, creation, and analysis of histology
sections is inherently invasive, time-consuming, and qualitative. The goal of this proposal is to develop an image
analysis pipeline that can provide automated quantitative analysis of wounds and lay the groundwork for a non-
invasive real-time “optical biopsy” that can provide information identical to standard histopathology. Our central
hypothesis is that artificial intelligence approaches using deep learning convolutional neural networks can be
coupled with in vivo multiphoton microscopy and existing quantitative image analysis methods to achieve this
goal with the same accuracy as traditional biopsies with histological staining and expert analysis. In Aim 1, we
will training and validate neural networks capable of segmenting and quantifying standard wound histology based
on training from three independent wound healing research labs. In Aim 2, we will adapt this network to perform
segmentation and quantification of in vivo label-free multiphoton microscopy images of skin wounds to provide
rapid readouts of wound organization and metabolic function. Finally in Aim 3, we will develop and validate a
network capable of generating virtual histology images from our stain-free non-invasive in vivo MPM images,
which can be coupled with the networks developed in Aim 1 and 2 to provide a comprehensive assessment of
wound microstructure and metabolism. In the near-term, this proposal will develop a series of robust analysis
tools that can be applied to existing H&E-stained or unstained skin tissue sections commonly studied by wound
healing researchers. In the long-term, the combination of label-free multiphoton microscopy and machine
learning-based image analysis will enable completely non-invasive wound histology that can be performed in
real-time at the point of care to guide debridement and wound care.
