Semester
Summer
Date of Graduation
2022
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Lane Department of Computer Science and Electrical Engineering
Committee Chair
Gianfranco Doretto
Committee Co-Chair
Donald Adjeroh
Committee Member
Donald Adjeroh
Committee Member
Omid Dehzangi
Abstract
Automated cellular instance segmentation is a process that has been utilized for accelerating biological research since before the deep learning era, and recent advancements have produced higher quality results with less effort from the biologist. Most current endeavors focus on completely cutting the researcher out of the picture by generating highly generalized models. However, these models invariably fail when faced with novel data and effectively opt to miss out on the full capabilities of deep learning in pursuit of this goal. In our work, we demonstrate how, with even a minimal amount of annotated data, dominant approaches in this space can achieve much better performance in many cases and cover blind spots that generalized models cannot accommodate. Utilizing specialized contrastive losses, our few-shot domain adaptation method not only performs well on all datasets tested; it even approaches the level of networks trained directly on the target datasets. In several ways, this work extends the current aims of generalized cellular instance segmentation techniques and paves a path toward an optimal balance between model performance and expert-level annotation.
Recommended Citation
Keaton, Matthew R., "A Domain Adaptation Approach for Segmenting Cell Instances in Microscopy Data" (2022). Graduate Theses, Dissertations, and Problem Reports. 11368.
https://researchrepository.wvu.edu/etd/11368
Embargo Reason
Patent Pending