Synthetic training of machine learning algorithms for holographic cell imaging

Brad Bazow; Thuc Phan; Van Lam; Christopher B. Raub; George Nehmetallah

doi:10.1117/12.2585825

12 April 2021 Synthetic training of machine learning algorithms for holographic cell imaging

Brad Bazow, Thuc Phan, Van Lam, Christopher B. Raub, George Nehmetallah

Author Affiliations +

Proceedings Volume 11731, Computational Imaging VI; 1173102 (2021) https://doi.org/10.1117/12.2585825
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

In this work we seek to demonstrate acceptable classification performance for classifiers trained using augmented training data of synthetic cells imaged by simulated holography and evaluated using experimentally collected holographic data. In particular, we utilize experimentally collected DHM phase maps derived from the MDA-MB-231 breast cancer cell and immortalized human gingival fibroblast (GIE) cell lines. Automated segmentation was performed using a floodfill clustering approach and experimental feature distributions were used to generate statistically random synthetic cell realizations for each class.

Conference Presentation

Citation Download Citation

Brad Bazow, Thuc Phan, Van Lam, Christopher B. Raub, and George Nehmetallah "Synthetic training of machine learning algorithms for holographic cell imaging", Proc. SPIE 11731, Computational Imaging VI, 1173102 (12 April 2021); https://doi.org/10.1117/12.2585825

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