Mid-Infrared Spectroscopic Imaging (MIRSI) integrates the molecular precision of vibrational spectroscopy with the comprehensive spatial resolution offered by microscopy. The merger of machine learning with MIRSI has empowered the process of discerning tissue subtypes and grading cancers in a quantitative, label-free way. We plan to showcase findings derived from subtyping and segmenting tissues from ovarian, cervical, and endometrial cancers using an advanced high-resolution photothermal MIRSI technology. Moreover, we will exhibit imaging data along with analysis results from clinical bone samples and outline the characteristics of fibrosis.
Mid-infrared spectroscopic imaging (MIRSI) combines the molecular specificity of vibrational spectroscopy with the spatial detail provided by microscopy. Combining machine learning and MIRSI has facilitated the identification of tissue subtypes and cancer grades in a label-free and quantitative manner. We will present results from tissue subtyping and segmentation in ovarian, cervical, and endometrial cancer tissue with a new, high-resolution photothermal MIRSI technology. We will also show imaging data and analysis results from clinical bone samples and characterize fibrosis.
The absorption of mid-infrared light has the specificity to enable biochemical identification and characterization of tissue. Mid-infrared spectroscopic imaging (MIRSI) combines the molecular specificity of vibrational spectroscopy with the spatial detail provided by microscopy. The combination of machine learning and MIRSI has facilitated the identification of tissue sub-type and cancer grade in a label-free and quantitative manner. We will present results comparing various MIRSI technologies and discuss the advantages of each technology. We also present imaging data and results of high-resolution MIRSI of ovarian, cervical, and bone samples.
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