Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Bioinformatics (MSBI)

Department

Computer Science

First Advisor

Dr. Philip Heller

Second Advisor

Dr. Thomas Austin

Third Advisor

Dr. Crawford Drury

Keywords

deep learning, convolutional neural network, Mask R-CNN, coral resilience

Abstract

As ocean temperatures rise, coral bleaching is becoming more frequent and severe. Selective breeding experiments show promise for enhancing coral resilience, but scaling these projects is hindered by the labor-intensive nature of taking numerous time series measurements as corals grow. Automating this process with computer vision is one solution to this bottleneck, and to our knowledge, no such tool exists at present. To fill this gap, we have trained a set of machine learning models, based on the Mask R-CNN framework, for segmenting juvenile corals in lab-based coral resilience research. This work shows that retraining the Mask R-CNN architecture through transfer learning results in a model capable of accurately segmenting corals in advanced developmental stages. To correctly segment images from earlier stages, an enlarged training dataset or a distinct model focused on early stages is necessary. The preliminary outcomes presented here suggest that the method holds potential.

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