HypoQuantyl

Image analysis tool used for tracking localized growth of hypocotyls using high-throughput cloud computing. This program uses a variety of machine learning algorithms to identify a hypocotyl, extract the growth region, form a midline, then quantify the elemental growth rate in which cellular elongation occurs at many locations along that midline.

Note

Used BFG to remove all objects > 500K in size [07-09-2019]

Summary [re-iterate above description and expand on use, novelty, and similar software]

A) What is this program for?
Learning algorithms: predict contour, predict ending of midline
Image analysis: select points along seedling
Quantification: Localized measurements of midline, model growth of growing seedlings
Miscellaneous features: late-decision segmentation for error checking, ease of loading data
Output Data: models full developmental process of a growing, de-etiolating hypocotyl

B) How does this program work?
User stores time-lapse image stacks of a single type in individual folders
A full experiment is a set of individual image stacks in a parent folder
Multiple experiments can be loaded as well

C) Instructions for using HypoQuantyl can be found in HOWTO.md
- Sample data can be found in Sample Data

Getting Started

Dependencies

• Lowest usable MATLAB version
• Run scripts for toolbox dependencies (like from QuantDRaCALA)
• Maybe something about CyVerse if necessary?

Installation

• (to-do) Install program as Matlab executable
  
• (to-do) Program runs on CyVerse
  

Version Information

02/05/2018 - HypoQuantyl Version 0.1 [ date created ] 02/06/2018 - HypoQuantyl Version 0.1 [ name chosen ] 02/22/2018 - HypoQuantyl Version 0.3 [ class structure finalized ] 05/24/2018 - HypoQuantyl Version 0.4 [ midpoint-normalization method ] 08/02/2018 - HypoQuantyl Version 0.5 [ routes method --> curves method ] 12/05/2018 - HypoQuantyl Version 0.6 [ optimized image read/write ] 02/18/2019 - HypoQuantyl Version 0.7 [ learning-based segmentation framework ]

Authors

Main Author

Julian Bustamante, Cellular and Molecular Biology Program (jbustamante@wisc.edu)
University of Wisconsin - Madison
Department of Botany

Contributing Author

Nathan Miller, Senior Scientist (ndmill@gmail.com)
University of Wisconsin - Madison
Department of Botany

License

MIT license found in LICENSE

Acknowledgements

• Guosheng Wu
  
• Nathan Miller
  

To-Do:

• Segmentation with Convolution Neural Net (CNN)
  
  • CNN Architecture
    
  • Feature analysis
    
• Junction Finder with CNN
  
  • Click Method
    
  • Slice Method
    
• Midline Region Splitter and Feature Identifier
  
  • Track patches between frames
    
• Kinematics Analysis
  
  • Elemental Growth Rate methods
    
• Validation Methods
  
  • Manual methodology
    
• Deployment to CyVerse
  
  • Compile to use with MCR
    
  • Synchronization with Open Science Grid
    
• True Application Testing
  
  • CRY1 mutants
    
  • Cell Wall mutants
    

Class Structure and Main Functions

Main Classes

Class	Description
HypoQuantyl	Initializes program and loads Experiment folders
Experiment	Loads folders containing multiple image stacks
Genotype	Loads image stacks containing growing seedlings
Seedling	Represents a single seedling throughout a time-lapse
Hypocotyl	Represents the hypocotyl portion of a single seedlings
CircuitJB	Main contour of a hypocotyl, segmented by anchor points
Route	Individual segments around a hypocotyl's contour

Algorithms

Algorithm	Description
FindHypocotyl	Identifies region on a seedling containing the hypocotyl
TrackHypocotyl	Keeps track of hypocotyl during de-etiolation process
FindGoodFrames	Runs error-checking of each frame for each Seedling
IntegrationAlgorithm	Integration method for measuring growing Hypocotyl

Implementing to Cyber Infrastructure

Infrastructure	Description
Create accounts	University Network, CyVerse, HTCondor
Set up submit files	Holds MATLAB MCR, HypoQuantyl, Data, I-Commands
Run Test Data	Run HypoQuantyl on Sample data on cloud computing environment