Graduation Semester and Year
Spring 2024
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Dr. Jacob Luber
Abstract
This study focuses on the performance analysis of a diffusion model utilized for generating realistic three-dimensional (3D) reconstructions from two-dimensional (2D) microscopy data. The research aims to assess and enhance the model's efficacy in predicting various features such as volume, surface area, curvature, and surface roughness of the reconstructed shapes. Drawing inspiration from previous work on Transformer architectures, modifications are proposed to optimize the training stability of the diffusion model. Specifically, two methods are implemented and evaluated to improve the model's performance in predicting 3D shapes from 2D microscopy images. This study focuses on the performance analysis of a diffusion model utilized for generating realistic three-dimensional (3D) reconstructions from two-dimensional (2D) microscopy data. The research aims to assess and enhance the model's efficacy in predicting various features such as volume, surface area, curvature, and surface roughness of the reconstructed shapes. Relative errors for the predicted features are calculated as part of experimental evaluations, which shed light on the precision and accuracy of the model. The suggested approaches are evaluated against benchmark datasets and biomedical imaging-related tasks. In comparison to the baseline design, the study's findings show improvements in one of the methods in the model's performance metrics. With implications for a range of biomedical imaging applications, these findings advance the capabilities of diffusion models in producing high-fidelity 3D reconstructions from 2D microscopy data. The study also emphasizes how crucial performance improvements are for biomedical imaging, especially when it comes to 3D reconstruction. Understanding biological processes, disease mechanisms, and cellular structures from 2D microscopy images requires accurate and efficient reconstruction.
Keywords
Diffusion model
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Jain, Aarushi, "Performance analysis and improvements in diffusion model which predicts 3D shapes from 2D microscopy images" (2024). Computer Science and Engineering Theses. 6.
https://mavmatrix.uta.edu/cse_theses/6