Automatic and Label-Free Analysis of the Microstructure Feature Differences Between Normal Brain Tissue, Low-Grade, and High-Grade Gliomas Using the Combination of Multiphoton Microscopy and Image Analysis

Abstract

Accurate intraoperative identification of gliomas is of utmost importance. This task often remains a challenge for the pathologist and neurosurgeon because of the absence of full intraoperative microstructure feature details of the tumor. Here, multiphoton microscopy (MPM), based on second harmonic generation (SHG) and two-photon excited fluorescence (TPEF), is applied for label-free detecting the microstructure feature differences between normal brain tissue, low-grade, and high-grade gliomas. MPM can not only capture the difference of their qualitative microstructure features such as increased cellularity, nuclear atypia, microvascular proliferation, and necrosis that are significant for diagnosing and grading of glioma, but also visualize some additional features such as collagen deposition that cannot be seen by conventional methods. In addition, automated image analysis algorithms are developed to automatically and accurately calculate the quantitative diagnostic features: collagen content, the number and area of nuclei to further quantitatively analyze the microstructure features difference of collagen deposition, cellularity, and nuclear atypia between normal brain tissue, low-grade, and high-grade gliomas. With the development of two-photon fiberscope, combined MPM and image processing techniques may become an imaging tool for assisting intraoperatively diagnosing and grading gliomas.