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Manual Segmentation of the Endoplasmic Reticulum in Cells to Aid in Computer learning

Undergraduate #60
Discipline: Biological Sciences
Subcategory: Cell and Molecular Biology
Session: 2
Room: Exhibit Hall

Marissa Schultz - University of Texas at San Antonio
Co-Author(s): Dr. Robert Miranda, Palo Alto college



Etch A Cell-ER is a citizen scientist project that I have been working on since the summer of 2021 that assists scientists by analyzing cell images and manually segmenting (tracing) the endoplasmic reticulum (ER) in order to help decrease the number of images needed to be analyzed by researchers, as well as to aid the computer in learning how to segment the ER on its own. I hypothesized that increasing the number of endoplasmic reticulum manually segmented in images will help train computers to automatically segment this organelle in cells. With the use of manual segmentation, I was able to analyze 311 images and individually trace a total of 10558 endoplasmic reticulum. A future goal of this project is to obtain a sufficient amount of images that are analyzed with tracing and segmentation. With enough images analyzed, the computer will eventually be able to learn how to segment the ER automatically and could create a surge of biological information that would give us a better understanding of the endoplasmic reticulum and how diseases affect this organelle.

Funder Acknowledgement(s): National Science Foundation Louis Stokes Alliances for Minority Participation grant (Award No. 2008428)

Faculty Advisor: Marsha Adrian, myaklin@alamo.edu

Role: I first completed background research on what the endoplasmic reticulum (ER) looks like under a microscope in order to properly identify them in the images. On the Etch A Cell- ER website I located and analyzed 311 images and within these images the total number of ER segmented overall was 10,558. I then created/continuously updated a detailed spreadsheet with all the images I analyzed and how many ER I observed/traced in each image in order to keep an organized database. With all of my collected data I then made an abstract and Power point presentation which included the results that I gained from the spreadsheet.

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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