Data Salon Friday, Nov. 6: Segmentation of Stimulated Raman Scattering Microscopy Images using Deep Neural Networks

Binghamton University Assistant Professor of Biomedical Engineering Frank Lu, Associate Professor of Computer Science Kenneth Chiu and undergraduate student Adiel Felsen will speak via Zoom at 2 p.m. Friday, Nov. 6. The topic of their talk will be "Segmentation of Stimulated Raman Scattering Microscopy Images using Deep Neural Networks."

Abstract: Medical procedures are often limited by the precision and accuracy of modern equipment. Acquiring dyed images of cancer cells can often require upwards of half an hour in a lab using traditional histological staining. During brain surgery procedures, every minute spent analyzing cancer tissue is wasted. A promising alternative, stimulated Raman scattering (SRS) microscopy uses microscopic lipid and protein vibrations to provide rapid cell nuclei imaging without the use of stains or other processes (label-free).

However, when identifying cancer cells, it is critical for doctors to be able to visualize the density and distribution of cell nuclei. Basic thresholding techniques are commonly used for counting the nuclei of H&E stained cells. Unfortunately, the low signal to noise contrast of SRS images hampers these techniques. The contrast between cell nuclei and their cell membrane is far less for SRS images versus traditional staining techniques.

Our research focuses on applying image segmentation techniques to highlight and count cell nuclei from SRS microscopy images. We utilize the U-Net and Mask R-CNN image segmentation architectures to segment cell nuclei from their surrounding membrane. With this result, we accurately produce many critical statistics such as nuclei counts and area. We also generate images that mimic the contrast of H&E stained cells.

Our machine learning techniques allow SRS microscopy to match the accuracy of histological staining in much less time. This research demonstrates the advantage of applying machine learning techniques in a surgical setting.

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