scellseg 0.1.8

a specializable cell segmentation tool

Scellseg

A style-aware cell instance segmentation tool with pre-training and contrastive fine-tuning

Description

We proposed a "pre-trained + fine-tuning" pipeline for cell instance segmentation. To make Scellseg easy to use, we also developed a graphical user interface integrated with functions of annotation, fine-tuning and inference. Biologists can specialize their own cell segmentation model to conduct single-cell image analysis.

Install

Operating system: It has been tested on Windows 10. Theoretically, it can work on any system that can run Python.

Programing language: Python.

Hardware: >= 8G memory, equipped with a CPU with Core i5 or above.

Our Environment: Python --3.7.4, CUDA --10.1.243, GPU：Nvidia 2080Ti.

Before installation, please check whether you can use conda environment

conda create --name scellseg_env python=3.7
activate scellseg_env
pip install scellseg --default-timeout=10000

If you get an "Timeout error", increase the number of --default-timeout and try again, for example:

pip install scellseg --default-timeout=100000

After installing scellseg successfully, you can start the GUI through:

activate scellseg_env
python -m scellseg

If you have a GPU device and "Use GPU" in GUI is disabled, you should check the version of "torch" and re-install the correct torch version suitable for your CUDA version (use "nvcc -V" to check your CUDA version), for example:

nvcc -V
pip install torch==1.7.1+cu101 -f https://download.pytorch.org/whl/cu101/torch_stable.html

How to use GUI

1. Annotation

​ Besides the basic function of Cellpose,

​ a) You can modify the mask of instance directly in pixel level without deleting it and drawing it from scratch. You can check "Edit mask" or [E], in this mode, you need firstly select a mask you wanted to edit, the selected mask will be highlighted, use right-click to add pixels and Shift+right-click to delete pixels

​ b) You can also take an overall look at of masks you have labelled with a list for each image, each index corresponds to a instance, you can pitch on and add notes to it, besides, the list can be saved and loaded next time

​ c) Drag a image/mask or a folder is supported, for a image, we autoload its parent directory, for a mask, we autoload its corresponding image and its parent directory. You can use [ctrl+←/→] to cycle through images in current directory

​ d) You can save the masks in ".png" format

2. Fine-tuning

​ a) You should prepare your data in one folder with your experiment name like "mito-20211116", here we call it parent folder. Into this folder, it should contain a shot subfolder and a query subfolder. The shot subfolder contains images and the corresponding labelled masks, the query subfolder contains the images you want to segment. Into the shot subfolder, images should be named with "_img" suffix and masks should be named as "_masks" suffix. Except the suffix, the name of image and mask should be identical, for example, 001_img and 001_masks, notably, 001_masks should not be named as 001_cp_masks or 001_img_cp_masks (You should rename your masks name after annotation). Into the query subfolder, images should be named with "_img" suffix

​ b) Click "Dataset path" to choose the parent folder of your dataset, such as "mito-20211116"

​ c) Set the channel you want to segment, you can also provide a chan2 like nuclei channel for better learning

​ d) Set the epoch you want conduct, the default value is 100, which was used in our paper. You can increase the number for adequate training

​ e) Set the batch size according to your own GPU, the default value is 100, which was used in our paper

​ f) You can select different pre-trained model ("Scellseg", "Cellpose", or "Hover") and fine-tuning strategy ("contrastive fine-tuning" or "classic fine-tuning")

​ g) Click "Start fine-tuning" button to start fine-tuning. After fine-tuning, it will show the saved path of the model file in the bottom of display window (saved at a subfolder in parent folder named "fine-tune", mito-20211116/fine-tune")

3. Inference

​ a) There are two modes for inference, (1) run segmentation for image in window (2) batch segmentation

​ b) If you want to conduct batch segmentation, click "Data path" to choose the parent folder of your dataset, such as "mito-20211116" , and set the adequate batch size according to your own GPU

​ c) You can choose your own model file for inference, the default is the pre-trained Scellseg model file

​ d) The default "model match threshold" is set to 0.4 and "cellprob threshold" is set to 0.5, which was used in our paper, you can change it for better performance

​ e) Set the channel you want to segment, you can also provide a chan2 like nuclei channel for better learning, you should set the same setting as fine-tuning process

​ f) You can get each instance image after inference, click "Data path" to choose the query folder of your dataset, such as "mito-20211116/query", the output files will be saved at a subfolder in parent folder named "single", mito-20211116/single"

Declaration

Our pipeline is heavily based on Cellpose and we also referred to the following projects: Hover-Net: https://github.com/vqdang/hover_net Attention-Unet: https://github.com/ozan-oktay/Attention-Gated-Networks