Non-invasive automated wound analysis via deep learning neural networks

项目总结： 每年，数以百万计的美国人患上慢性伤口，这需要先进的伤口护理，而 估计每年耗资500亿美元。然而，我们对慢性伤口的理解以及如何治疗它们 由于缺乏客观地描述和测量伤口特征的既定方法，这一点一直受到限制。 临床和研究实验室对伤口的详细评估通常是通过组织学分析进行的 组织活组织检查。这些信息可以洞察伤口中的细胞迁移、细胞增殖 在伤口的边缘，感染和纤维化。然而，组织学的收集、创造和分析 分区本身就是侵入性的、耗时的和定性的。这项提议的目标是发展一种形象 分析管道，可以提供伤口的自动化定量分析，并为非 侵入性实时“光学活检”，可提供与标准组织病理学相同的信息。我们的中央 假设使用深度学习卷积神经网络的人工智能方法可以 再加上体内多光子显微镜和现有的定量图像分析方法来实现这一点 目的通过组织学染色和专家分析，获得与传统活检相同的准确性。在目标1中，我们 是否将训练和验证能够分割和量化标准伤口组织学的神经网络 来自三个独立伤口愈合研究实验室的培训。在目标2中，我们将调整此网络以执行 皮肤创伤的体内无标记多光子显微镜图像的分割和量化 伤口组织和代谢功能的快速读数。最后，在目标3中，我们将开发并验证 能够从我们的无污渍非侵入性体内MPM图像生成虚拟组织学图像的网络， 它可以与目标1和目标2中开发的网络相结合，以提供对 伤口微结构和新陈代谢。在短期内，这项提议将制定一系列强有力的分析 可应用于创伤通常研究的现有H&E染色或未染色皮肤组织切片的工具 治疗研究人员。从长远来看，无标记多光子显微镜和机器的结合 基于学习的图像分析将实现完全非侵入性的伤口组织学，可以在 在护理点实时指导清创和伤口护理。

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.