DL_Particle_Detection

Deep learning methods for particle detection in microscopy images.

It covers four deep-learning model, including DetNet[1], deepBlink[2], superpoint[3], and HigherHRNet[4]. HigherHRNet[4] was modified and renamed as PointDet in this repository.

TODO

• Test PointDet using 16bit image.

Environment

The code is developed using python 3.7.3 on Ubuntu 18.04. NVIDIA GPUs are needed. The code is developed and tested using 1 NVIDIA GeForce RTX 2080 Ti card.

Quick Start

Installation

1. Create a virtual environment

conda create -n dl_particle_detection python==3.7.3 -y
conda activate dl_particle_detection

2. Install pytorch==1.8.0+cu111 torchvision==0.9.0+cu111

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html

3. Clone this repo

git clone https://github.com/imzhangyd/DL_Particle_Detection.git
cd DL_Particle_Detection

4. Install dependencies:

pip install -r requirements.txt

Data preparation

Make your dataset look like this:

|-- data
`-- |-- dataset1
    `-- |-- train
        |   |-- image001.tif
        |   |-- image001.csv
        |   |-- image002.tif
        |   |-- image002.csv
        |   |-- ...
        `-- val
        |   |-- image001.tif
        |   |-- image001.csv
        |   |-- image002.tif
        |   |-- image002.csv
        |   |-- ...
        `-- test
        |   |-- image001.tif
        |   |-- image001.csv
        |   |-- image002.tif
        |   |-- image002.csv
        |   |-- ...

The .csv format:

x0,y0,0
x1,y1,0
x2,y2,0
...

The coordinate system is based on the left-top corner of the image, where the origin is located. The positive x-direction extends to the right, and the positive y-direction extends downward.

Example

• Train on your own dataset.
  We use the dataset generated by ISBI Particle Tracking Challenge Generator (ISBI PTCG). The example dataset (VESICLE, SNR=4) can be found at this onedrive link or Baidu Netdist.

python trainval.py \
--train_datapath='./data/train_VESICLE/SNR4/' \
--val_datapath='./data/val_VESICLE/SNR4/' \
--exp_name='VESICLE_SNR4_deepBlink'

• Eval. Assess the checkpoint performance on the validation set to determine the optimal threshold. Utilize this identified threshold to evaluate on the test set.

python infer_determine_thre.py \
--val_datapath='./data/val_VESICLE/SNR4/' \
--test_datapath='./data/test_VESICLE/SNR4/' \
--ckpt_path='.......' \
--exp_name='VESICLE_SNR4_deepBlink'

• Eval. Utilize one identified threshold to evaluate on the test set.

python infer_one_thre.py \
--test_datapath='./data/test_VESICLE/SNR4/' \
--ckpt_path='.......' \
--exp_name='VESICLE_SNR4_deepBlink'

• Only prediction without evaluation: Test on your own dataset using our pretrained model. The pretrained model trained on ISBI PTCG dataset can be found at this link.

python infer_one_thre_onlypred.py \
--test_datapath='./data/test_VESICLE/SNR4/' \
--ckpt_path='.......' \
--exp_name='VESICLE_SNR4_deepBlink'

Detect and track on your own data

Refer to Tracking private data.md.

Reference

[1] Wollmann, Thomas, et al. "DetNet: Deep neural network for particle detection in fluorescence microscopy images." 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, 2019.
[2] Eichenberger, Bastian Th, et al. "deepBlink: threshold-independent detection and localization of diffraction-limited spots." Nucleic Acids Research 49.13 (2021): 7292-7297.
[3] DeTone, Daniel, Tomasz Malisiewicz, and Andrew Rabinovich. "Superpoint: Self-supervised interest point detection and description." Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2018.
[4] Cheng, Bowen, et al. "Higherhrnet: Scale-aware representation learning for bottom-up human pose estimation." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.