MT_CrossingAnalysis

This repo propose a pre-trained model DGF-Net for microtubule segmentation.

Architecture of the DGF-Net

How to use

Dependencies

The main dependencies are as follows:

Pytorch
Python 3
CV2

Model

You can download the pre-trained model from here.

See model.py for details

Data

Please put your source images into folder images (default), and the final segmentation images will be save in folder segmentations (default).

Note: the size of the input images should not larger than 1024*1024.

Quick start

run inference.py

python inference.py -t 0.6 --save_dir './segmentation' --img_dir './images' --img_type 16

You can use -t to change the threshold for different segmentation results, use --save_dir to create the saving folder, use --img_dir to change your source image dir, use --img_type to change the type of images, default uint16.

(Note that --img_type must match the soure image's type)

Contributing

Code for this projects developped at CBMI Group (Computational Biology and Machine Intelligence Group).

CBMI at National Laboratory of Pattern Recognition, INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES

Bug reports and pull requests are welcome on GitHub at https://github.com/cbmi-group/MT_CrossingAnalysis