LiteLoc

LiteLoc is a Python and Pytorch based scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy (SMLM).

LiteLoc includes a scalable and competitive data analysis framework and a lightweight deep learning network which has small number of parameters (only 1.33 M) and low computational complexity (71.08 GFLOPs, 10 images with 128x128 pixels) while maintaining comparable or even better localization precision.
With the help of high parallelism between the data loader/analyzer/saver processes, the total analysis speed is ~25 times faster than that of DECODE and more than 560 MB/s data analysis throughput could be achieved with eight NVIDIA GTX 4090 GPUs.

Getting Started

Installation

1. Clone the repo

   git clone https://github.com/Li-Lab-SUSTech/LiteLoc.git
   cd LiteLoc-main
   

2. Create a virtual environment

   conda create -n liteloc_env python==3.8.19
   

3. Activate liteloc environment and install the required packages

   conda activate liteloc_env
   pip install -r requirements.txt
   conda install -c turagalab -c conda-forge spline
   

The project files should be organized as the following hierarchy:

.
|-- PSF_Modeling                                
|   |-- Fit_PSF_model
|       |-- calibrate_psf_model_GUI.m          // MATLAB GUI for vectorial PSF calibration from beads images.
|       `-- ...
|-- vector_psf                                 // module for generating vectorial PSF. 
|-- spline_psf                                 // module for generating C-spline interpolation PSF (from DECODE).
|-- demo                                       // several demos for showing how to use LiteLoc to train and infer.
|   |-- demo1_astig_npc                        // demo for training and inference of astigmatic PSF-based NPC imaging experiments.
|   |-- demo2_tetrapod_npc                     // demo for training and inference of Tetrapod PSF-based NPC imaging experiments.
|   `-- demo3_uipsf                            // demo for training and inference of uiPSF.
|-- network
|   |-- liteloc.py                             // architecture of LiteLoc network.
|   |-- loc_model.py                           // entire training process.
|   |-- loss_utils.py                          // loss function.
|   |-- multi_process.py                       // scalable and competitive inference framework.
|   |-- decode.py                              // architecture of DECODE network.
|   |-- loc_model_decode.py                 
|   |-- multi_process_decode.py
|   `-- ...
|-- utils
|   |-- data_generator.py                      // generate training data.
|   |-- eval_utils.py                          // evaluate localization results and output metrics.
|   |-- help_utils.py                          // various functions that can be easily invoked.
 `-- ...
|-- calibrate_mat
|   |-- astig_npc_psf_model.mat                // PSF model of demo1.
|   |-- tetrapod_npc_psf_model.mat             // PSF model of demo2.
|   `-- uipsf_model.mat                        // PSF model of demo3.
|-- datasets                                   // directory for placing inference dataset.
|-- results                                    // directory for placing training results.

Demos

We provide several examples in demos directory, illustrating how to train LiteLoc and infer based on different PSF models. For all demos:

• Please download data from and put in demos directory.
• Training and inference results will be automatically saved in results directory.
• Localizations are saved as .csv file.

We recommend users to use SMAP software for post-processing and rendering the results. A XX.jupyter file is also provided to show the intermediate results of the entire process.

Demo1: LiteLoc for astigmatic PSF-based NPC imaging.

Demo1 is based on the experimental Nup96 NPC dataset with a 1.4 μm astigmatic PSF.

• train_params_demo1.yaml: Parameter setting for training LiteLoc, which will be automatically invoked before training.
• liteloc_train_demo1.py: Program for training LiteLoc.
• infer_params_demo1.yaml: Parameter setting for inference.
• liteloc_infer_demo1.py: Program for inference.
• XX.jupyter: To show the intermediate results of the entire process.

Demo2: LiteLoc for Tetrapod PSF-based NPC imaging.

Demo2 is based on the experimental Nup96 NPC dataset with a 6μm Tetrapod PSF. The files in this directory are the similar to those in demo1_astig_npc.

License

Distributed under the project_license. See LICENSE.txt for more information.

Contact

For any questions about this software, please contact Li-Lab.