Optimization of U-Net: convolutional networks for U87 human glioblastoma cell line segmentation

Esra Sengul; Meltem Elitas

doi:10.1117/12.2595132

3 September 2021 Optimization of U-Net: convolutional networks for U87 human glioblastoma cell line segmentation

Esra Sengul, Meltem Elitas

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118041G (2021) https://doi.org/10.1117/12.2595132
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

Glioblastoma multiforme (GBM) is one of the most aggressive primary brain tumors with its extreme proliferation and invasiveness. U87 human glioma cell line is one of the best representative cell lines for GBM with its extremely heterogenous and frequently altered morphologies. Quantification of heterogeneity and morphological changes of U87 glioma cells are mostly based on manual analysis. Therefore, automated image segmentation and analysis approaches are crucial. Here, we implemented U-Net algorithm for segmentation of U87 glioma cells and obtained 0.06% loss and 97.3% accuracy values. Next, we integrated Chan-Vese, K-means, and Morphological Filtering. Finally, we compared the performances of these approaches. We believe that our preliminary data might contribute to development of automated, reliable, accurate, and cell type specific image segmentation tools.

Conference Presentation

Citation Download Citation

Esra Sengul and Meltem Elitas "Optimization of U-Net: convolutional networks for U87 human glioblastoma cell line segmentation", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118041G (3 September 2021); https://doi.org/10.1117/12.2595132

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