Author ORCID Identifier
Semester
Summer
Date of Graduation
2024
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, Ph.D
Committee Co-Chair
Donald Adjeroh, Ph.D
Committee Member
Donald Adjeroh, Ph.D
Committee Member
Nima Karimian, Ph.D
Abstract
In recent years, there has been an upward trend of utilizing deep learning to automate cell segmentation processes. As global storage capacities grow exponentially, so have microscopy data collections become larger and more frequent. To benefit from them, accurate and precise quantitative analysis tools like cell instance segmentation have become necessary. However, the highly variable nature of these data collections necessitates retraining segmentation models to maintain high accuracy on new data collections. This process is time-consuming and labor-intensive since a user must annotate much of the new data, usually under the supervision of a medical professional. The problem is further exacerbated when segmenting cells with elongated and non-convex morphology, like bacteria cells.
In mitigating these concerns, we propose reducing the amount of annotation and compute power needed to retrain the model by introducing a few-shot domain adaptation approach that requires the annotation of only one to five cells of the new data. First, we rely on a robust and precise segmentation method trained on extensive source data and capable of handling highly diverse cell morphologies. Second, we take a few-shot learning approach, where given a target dataset that is distributed differently from source data, we require a user to label only a minimal amount of target data. We then set up a contrastive prediction task by introducing new losses that pull the representation of positive samples in a target domain closer to samples of the same class in a source domain while simultaneously pushing them apart from negative source samples using kernels as a similarity measure. Furthermore, we comprehensively studied the best kernel composition method for combining kernels defined on two inhomogeneous pairs of quantities. Our approach quickly adapts the model to maintain high accuracy, and our results show a significant boost in accuracy after adaptation to very challenging bacteria datasets.
Recommended Citation
Brume, Voke Rotimi, "A Domain Adaptation Approach for Morphology-Independent Cell Instance Segmentation" (2024). Graduate Theses, Dissertations, and Problem Reports. 12607.
https://researchrepository.wvu.edu/etd/12607
Comments
Thank you God!