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.
Mid-infrared spectroscopic imaging (MIRSI) combines the molecular specificity of vibrational spectroscopy with the spatial detail provided by microscopy. Chemical information from each pixel of an image is used in a machine-learning framework to perform tissue sub-type identification, and recognition of tissue disorders. Recent developments in infrared imaging have resulted in an order of magnitude improvement in resolution relative to FT-IR. We will present and discuss results from imaging of ovarian and bone tissue using FT-IR imaging, and a new photothermal absorption technique for identifying tissue sub-types in an accurate, quantitative, label-free manner.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.