Animal Internal Motion Analysis with Unsupervised Machine Learning Methods

Understanding the complex internal motions within biological systems—from cellular migrations during development to repeated contractions in muscle tissue—is essential for comprehending the fundamental mechanisms that drive life processes. This report presents innovative unsupervised machine learning methods to explore the dynamics of cellular and tissue motion and their implications. We first address cellular motion, focusing on long-term movements. While our laboratory's previously established Minimum-Cost Circulation-Based Framework provides a foundational approach, it suffers from limitations in robustness due to inadequate accuracy in cell segmentation. To address these shortcomings, we introduce PrinCut-Auto, a cutting-edge method leveraging Multi-scale Principal Curvature (MSPC) for precise cell identification, integrated with min-cut optimization and order-statistics testing to significantly advance 3D cell segmentation technology. PrinCut-Auto can address the limitations in the accuracy and robustness of the framework and we provide comprehensive experimental evidence of its efficacy. Beyond cellular movements, the study also addresses the analysis of tissue dynamics, with a particular emphasis on rapid and repetitive motions such as muscle contractions. To capture these complex patterns effectively, we developed BWMquant, a tool capable of efficient and accurate motion correction, as well as the detection of recurring motion patterns within tissues. The impact of these tissue dynamics on biological processes is discussed, with specific experimental setups and results demonstrating the crucial interplay between mechanical movements and biological functionality. The report concludes with a discussion on future directions, emphasizing the potential for these methodologies to enhance biological research. This study not only advances our understanding of biological internal motion but also sets the stage for future innovations in the field.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Zheng, Wei

Chairs dc:contributor.committeechair

• Wang, Yue J.
• Yu, Guoqiang

Committee members dc:contributor.committeemember

• Lin, Zin
• Zhang, Richard
• Ji, Bo

• optimization
• statistics
• machine learning
• image segmentation
• image registration

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:43106

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/134955