Huining Liang has worked on a new deep-learning Transformer-based segmentation technique. Since no one has applied it to stromule data before, this helps plant science researchers achieve a higher order, high-throughput multiplexing. Huining has also applied Transformers for tracking. The overall impact includes the improvement over the previous pipeline, which involves UNet and traditional computer vision-based methods. The current pipeline achieved higher accuracy than the earlier work. Furthermore, Huining classified stromules and their motion using a curve fitting based algorithm for modeling deformable objects. Then she evaluated clustering by unsupervised machine learning and more classic methods, such as DBSCAN and K-means. The cluster analysis has the potential to reveal novel biological types of stromules based on how they move.
Dr. Kambhamettu directs the Video/Image Modeling and Synthesis (VIMS) Lab, which has ten PhD students working on deep learning approaches. The approaches developed in this project open a way to incorporate some of the concepts in other projects.
Dr. Caplan directs a Bio-Imaging Center that is used by 19 different departments at the University of Delaware, spanning a wide array of disciplines. The approaches developed in this project can be translated to other projects in the network. Tracking the motion and deformation of objects in biological system is a common computational problem. Doing so makes it possible to look for significant differences after a biological system is altered by a treatment or a genetic knockout of a component. This is a powerful complement to other biological approaches and provides a new method for functional validation of genes or proteins involved in the movement of cellular components.
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Yes, Huining Liang will use this training in her Ph.D. work and to assist others in research computing, therefore, adding to our human resource infrastructure.
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In VIMS Lab, this project helps update the repository of techniques impacted by Huining’s work under CAREERS. It now includes transformer-based techniques as a contribution to this repository and further will be used in other collaborations. The cluster-based approaches will be added to the available approaches in the Bio-Imaging Center to examine biological motion data.
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The project that Huining Liang worked on developed the use of a latest deep learning technique and compared against the previous pipeline, and achieved improved results. It will assist her future innovation in deep learning and approaches being developed to modify the movement and positioning of chloroplasts to improve photosynthesis. The Caplan lab is currently investigating how beneficial microbes change chloroplast morphology and movement to improve crop yields. The approaches developed here can determine if a specific class of chloroplast movement or stromules increases or decreases in response to microbes. Thus, this project may potentially benefit crop production and food security which is a major societal impact.
Learn the knowledge of different clustering methods for data analysis.
Train a 3D UNet to perform 3D segmentation of microscopy images.
Extend and add new functions to the current image processing pipeline.
Facilitate research with machine learning methods and HPC resources.
Learn the curve fitting based algorithm for modeling deformable objects.
Apply the curve fitting algorithm to obtain stromule motion data from microscopy imagery.
****** From Huining's Exit Interview ******
This series of projects helped her to build a pipeline, modify and add additional functions to the pipeline. Using HPC resources allowed for the pipeline to use 3D features and more complex models reducing compute time from months to weeks to days. She will be graduating in Dec2026 with her PhD in Computer Vision.