A Machine Learning Model for Cellular Microscopy Segmentation
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Aponte, Emilio Aponte
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Abstract
Despite the number of new options biologists have to automate the task of
cell segmentation, virtually none have caught on and the majority of biomed-
ical research labs rely on human annotators to collect and analyze microscopy
data. The goal of this paper is to facilitate the process of cell segmentation
for researchers by providing a simple solution. We aim to build upon the ad-
vancements of SAMCell and increase its usability for common use in biomedical
research laboratories through the improvement of the model and development
of an accessible lightweight User Interface for users to make use of SAMCell in
a manner that minimally disrupts lab procedure and resources.
A modern challenge of cell culture analysis is a lack of standard metrics.
Scientists with experience in cell research form intuitions regarding cell culture
analysis, but their ability to confirm results are limited. If this project is suc-
cessful in attracting users in biomedical research, a secondary goal is to leverage
SAMCell, and this new ability to extract concrete cell masks, to standardize
the methods by which certain cell culture metrics are obtained.
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Undergraduate Research Option Thesis