Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection

Helen Zhang; Philip Cheney; Stephan Kanick; Hanh Le; David McClatchy III; Nian Liu; John Lu; Tae Joon Cho; Kimberly Briggman; David Allen; Wendy Wells; Brian Pogue; Jeeseong Hwang

doi:10.1117/12.3023601

13 March 2024 Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection

Helen Zhang, Philip Cheney, Stephan Kanick, Hanh Le, David McClatchy III, Nian Liu, John Lu, Tae Joon Cho, Kimberly Briggman, David Allen, Wendy Wells, Brian Pogue, Jeeseong Hwang

Author Affiliations +

Proceedings Volume PC12833, Design and Quality for Biomedical Technologies XVII; PC128330M (2024) https://doi.org/10.1117/12.3023601
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Hyperspectral dark-field microscopy (HSDFM) and analysis algorithms demonstrate classification of various tissue types, including carcinoma in human post-lumpectomy breast tissues. Performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with Monte Carlo simulations of the experimental data. For classification algorithms, two approaches, a supervised spectral angle mapper (SAM) algorithm and an unsupervised K-means algorithm, are used. The manually extracted endmembers of known tissue types were determined by the histopathology reading of the hematoxylin and eosin (H&E)-stained slides. Their associated threshold spectral correlation angles from the SAM algorithm for supervised classification make a good reference library that validates endmembers from the unsupervised algorithm. For unsupervised classification, a K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The endmembers extracted by the two methods agree with each other within less than 2% residual

Citation Download Citation

Helen Zhang, Philip Cheney, Stephan Kanick, Hanh Le, David McClatchy III, Nian Liu, John Lu, Tae Joon Cho, Kimberly Briggman, David Allen, Wendy Wells, Brian Pogue, and Jeeseong Hwang "Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection", Proc. SPIE PC12833, Design and Quality for Biomedical Technologies XVII, PC128330M (13 March 2024); https://doi.org/10.1117/12.3023601

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