Rina Bao (ctc682) [email protected] The codes contain the implementations of our below paper, please cite our paper when you are using the codes.
Bao R, Al-Shakarji NM, Bunyak F, Palaniappan K. DMNet: Dual-stream marker guided deep network for dense cell segmentation and lineage tracking. In IEEE International Conference on Computer Vision (ICCV) Workshop on Computer Vision for Automated Medical Diagnosis, pp. 3354-3363, (2021).
This codes is for ISBI2021 6th Cell Segmentation and Tracking Challenge secondary track
Using Anaconda 3 (or miniconda3) on linux, run the following:
The Anaconda environment was tested on Linux with CUDA 10.2.
conda env create -f environment.yml
conda activate cell
1> Download the pretrained HRNet on imagenet from their website.
cd training_codes
mkdir models_imagenet
https://github.com/HRNet/HRNet-Image-Classification/
download the HRNet-W32-C model and put the model in models_imagenet
2> For six settings in "GT", "ST", "GT+ST", "allGT", "allST", "allGT+allST":
cd generate_bash
bash allGT.sh bash allST.sh bash allGT+ST.sh
For each dataset configuration training,
bash $dataset.sh
cd inference_codes
Running all bash files for testing
Thanks!