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Mechanically controlled spectroscopic imaging for tissue classification
Author(s): Laura Connolly; Tamas Ungi; Andras Lasso; Thomas Vaughan; Mark Asselin; Parvin Mousavi; Scott Yam; Gabor Fichtinger
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Paper Abstract

PURPOSE: Raman Spectroscopy is amongst several optical imaging techniques that have the ability to characterize tissue non-invasively. To use these technologies for intraoperative tissue classification, fast and efficient analysis of optical data is required with minimal operator intervention. Additionally, there is a need for a reliable database of optical signatures to account for variable conditions. We developed a software system with an inexpensive, flexible mechanical framework to facilitate automated scanning of tissue and validate spectroscopic scans with histologic ground truths. This system will be used, in the future, to train a machine learning algorithm to distinguish between different tissue types using Raman Spectroscopy. METHODS: A sample of chicken breast tissue is mounted to a microscope slide following a biopsy of fresh frozen tissue. Landmarks for registration and evaluation are marked on the specimen using a material that is recognizable in both spectroscopic and histologic analysis. The slides are optically analyzed using our software. The landmark locations are extraction from the spectroscopic scan of the specimen using our software. This information is then compared to the landmark locations extracted from images of the slide using the software, ImageJ. RESULTS: Target registration error of our system in comparison to ImageJ was found to be within 1.1 mm in both x and y directions. CONCLUSION: We demonstrated a system that can employ accurate spectroscopic scans of fixed tissue samples. This system can be used to spectroscopically scan tissue and validate the results with histology images in the future.

Paper Details

Date Published: 8 March 2019
PDF: 9 pages
Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109512E (8 March 2019); doi: 10.1117/12.2512481
Show Author Affiliations
Laura Connolly, Queen's Univ. (Canada)
Tamas Ungi, Queen's Univ. (Canada)
Andras Lasso, Queen's Univ. (Canada)
Thomas Vaughan, Queen's Univ. (Canada)
Mark Asselin, Queen's Univ. (Canada)
Parvin Mousavi, Queen's Univ. (Canada)
Scott Yam, Queen's Univ. (Canada)
Gabor Fichtinger, Queen's Univ. (Canada)


Published in SPIE Proceedings Vol. 10951:
Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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