Analysis of deep ultraviolet fluorescence images for intraoperative breast tumor margin assessment

Tongtong Lu; Julie M. Jorns; Dong Hye Ye; Mollie Patton; Taly Gilat-Schmidt; Tina Yen; Bing Yu

doi:10.1117/12.2649552

6 March 2023 Analysis of deep ultraviolet fluorescence images for intraoperative breast tumor margin assessment

Tongtong Lu, Julie M. Jorns, Dong Hye Ye, Mollie Patton, Taly Gilat-Schmidt, Tina Yen, Bing Yu

Proceedings Volume 12368, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI; 1236806 (2023) https://doi.org/10.1117/12.2649552
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Positive margin status after breast-conserving surgery (BCS) is a predictor of higher rates of local recurrence. Intraoperative margin assessment aims to achieve negative surgical margin status at the first operation, thus reducing the re-excision rates that are usually associated with potential surgical complications, increased medical costs, and mental pressure on patients. Microscopy with ultraviolet surface excitation (MUSE) can rapidly image tissue surfaces with subcellular resolution and sharp contrasts by utilizing the nature of the thin optical sectioning thickness of deep ultraviolet light. We have previously imaged 66 fresh human breast specimens that were topically stained with propidium iodide and eosin Y using a customized MUSE system. To achieve objective and automated assessment of MUSE images, a machine learning model is developed for binary (tumor vs. normal) classification of obtained MUSE images. Features extracted by texture analysis and pre-trained convolutional neural networks (CNN) have been investigated for sample descriptions. A sensitivity, specificity, and accuracy better than 90% have been achieved for detecting tumorous specimens. The result suggests the potential of MUSE with machine learning being utilized for intraoperative margin assessment during BCS.

Conference Presentation

Citation Download Citation

Tongtong Lu, Julie M. Jorns, Dong Hye Ye, Mollie Patton, Taly Gilat-Schmidt, Tina Yen, and Bing Yu "Analysis of deep ultraviolet fluorescence images for intraoperative breast tumor margin assessment", Proc. SPIE 12368, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI, 1236806 (6 March 2023); https://doi.org/10.1117/12.2649552

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available