https://github.com/nelson-gon/cellseg

cellseg: Multiclass Cell Segmentation
https://github.com/nelson-gon/cellseg

biology deep-learning image-classification image-processing image-segmentation multiclass-classification neural-networks torch

Last synced: 9 months ago
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cellseg: Multiclass Cell Segmentation

• Host: GitHub
• URL: https://github.com/nelson-gon/cellseg
• Owner: Nelson-Gon
• License: mit
• Created: 2021-02-04T12:33:11.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2021-09-23T09:38:36.000Z (over 4 years ago)
• Last Synced: 2025-02-09T08:28:59.778Z (over 1 year ago)
• Topics: biology, deep-learning, image-classification, image-processing, image-segmentation, multiclass-classification, neural-networks, torch
• Language: Python
• Homepage: https://cellseg.readthedocs.io/en/latest/
• Size: 42.4 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: changelog.md
  • Contributing: .github/CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: .github/CODE_OF_CONDUCT.md

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README

          
# cellseg: Multiclass Cell Segmentation 

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# Development stage

- [x] Read Tiff Images

- [x] Read Non Tiff Images

- [x] Write Data Transformers and Loaders

- [ ] Write functional model plus scripts  

- [ ] Modify model weights/layers

- [ ] Read stacked tiff images/videos 

# Introduction

`cellseg` is a PyTorch (`torch`) based deep learning package aimed at multiclass cell segmentation. 

# Installation

```shell

pip install cellseg 

```

Or if you want to build from source 

```shell

git clone git@github.com:Nelson-Gon/cellseg.git

cd cellseg

python setup.py install 

```

# Usage

## Script mode 

**View images**

```shell

python -m cellseg -d data/train -t "image" -n 4 -s 512

```

To get help 

```shell

python -m cellseg --help

#usage: __main__.py [-h] -d IMAGE_DIRECTORY -s IMAGE_SIZE -t TARGET -n NUMBER

#

#optional arguments:

#  -h, --help            show this help message and exit

#  -d IMAGE_DIRECTORY, --image-directory IMAGE_DIRECTORY

#                        Path to image directory containing images and

#                        masks/labels

#  -s IMAGE_SIZE, --image-size IMAGE_SIZE

#                        Size of images

#  -t TARGET, --target TARGET

#                        Target images to show

#  -n NUMBER, --number NUMBER

#                        Number of images to show

```

## Programming mode 

**Importing relevant modules** 

```shell

from cellseg.data import DataProcessor

from cellseg.model import CellNet

from cellseg.utils import DataProcessor, show_images

```

**Creating a a model object**

```shell

my_model = CellNet()

```

**Load training data**

```shell

train_data = DataProcessor(image_dir="data/train/images", label_dir="data/train/images", image_suffix="tif")

```

**View loaded images or masks**

```shell

show_images(train_data, number = 8, target="image")

```

**Training**