TetAlyze

Reproduce figures in the paper

1. Setup the paths by running matlab setup.m.
2. To reproduce figures in the paper, run the corresponding MATLAB scripts, e.g. matlab fig*.m.

Image processing pipeline

0. Raw images used in this project are accessible in image repo.
1. Setup the paths by running matlab setup.m.
2. Edit the filename variable in the img_proc_pipeline.m script with the right image path.
3. rejection_threshold refers to the distance threshold. The BB with a distance to the fitted cell outline larger than this threshold will be rejected. The purpose is to reject the noise puncta inside the cell. The value can be 0.5 (strong rejection) ~ 2 (weak rejection) for WT cells without big concaves. Reduce this value can reject more Noisy puncta inside the cell, which can prevent rows produced inside the cell.
4. minRowLength refer s to the threshold of BB length. The row shorter than this threshold will be discarded.
5. minBBsInRow refers to the threshold of minimum number of BBs in a row. The row with no greater BBs than this value will be discarded.
6. Consider the importance of the correct row indexing, the code allows the user to correct the row indexing if necessary. When the Figure 2 window firstly promoted, please enter the current index of the real first index and press Enter to correct the index. If the current index is correct, enter nothing and hit Enter to continue.

Image processing was done on a desktop computer with a Core i9-10900K CPU @ 3.70GHz and 64 GB of RAM, running a Windows 11 operating system. The software was tested on MATLAB 2020b.

Required toolbox:

• Bioinformatics Toolbox

• Computer Vision System Toolbox

• Curve Fitting Toolbox

• Image Processing Toolbox

• Signal Processing Toolbox

• Statistics and Machine Learning Toolbox

• Wavelet Toolbox

Note that we include MATLAB Bio-Formats and Locally Weighted Polynomials toolbox packages in our repo here, and we thank the authors for developing those tools.

Images were 3D image stacks and stored in uint16 TIFF format with only one channel.

For an image with 500x500x75 pixels, it typically takes about 2 minutes to finish image processing.