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

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

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