Characterization of 3D organelle motion using curve fitting and clustering by unsupervised machine learning

Project Information

bioinformatics
Project Status: In Progress
Project Region: CAREERS
Submitted By: Anita Schwartz
Project Email: chandrak@udel.edu
Project Institution: University of Delaware
Anchor Institution: CR-University of Delaware
Project Address: Newark, Delaware. 19716

Mentors: Jeffrey Caplan
Students: Huining Liang

Project Description

This project will build upon our current work to track a filamentous biological structure called stromules. Previously, our approached used 2D maximum intensity projections of 3D data, which resulted in the loss of any 3D information. In this project, a 3D version of a hybrid CNN-Transformer architecture will be used for 3D segmentation of stromules. For tracking, we are using the tracking-by-attention paradigm which not only applies attention for data association but jointly performs tracking and detection, using TrackFormer, an end-to-end trainable MOT (multi-object tracking) approach based on an encoder-decoder Transformer architecture. That will be combined with a curve fitting based algorithm that was previously developed. The stromules will exhibit different motion behaviors and unsupervised machine learning clustering methods will be explored to find different classes of stromule motion.

Project Information

bioinformatics
Project Status: In Progress
Project Region: CAREERS
Submitted By: Anita Schwartz
Project Email: chandrak@udel.edu
Project Institution: University of Delaware
Anchor Institution: CR-University of Delaware
Project Address: Newark, Delaware. 19716

Mentors: Jeffrey Caplan
Students: Huining Liang

Project Description

This project will build upon our current work to track a filamentous biological structure called stromules. Previously, our approached used 2D maximum intensity projections of 3D data, which resulted in the loss of any 3D information. In this project, a 3D version of a hybrid CNN-Transformer architecture will be used for 3D segmentation of stromules. For tracking, we are using the tracking-by-attention paradigm which not only applies attention for data association but jointly performs tracking and detection, using TrackFormer, an end-to-end trainable MOT (multi-object tracking) approach based on an encoder-decoder Transformer architecture. That will be combined with a curve fitting based algorithm that was previously developed. The stromules will exhibit different motion behaviors and unsupervised machine learning clustering methods will be explored to find different classes of stromule motion.