Point-Supervised-Segmentation

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

Point-Supervised-Segmentation

★ 9 Open GitHub ↗

No description available.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Point-supervised segmentation of microscopy images and volumes via objectness regularization

## 1. Introduction

This project contains work of point-supervised segmentation of microscopy images and volumes via objectness regularization and re-implementations of PseudoEdgeNet([paper](https://arxiv.org/pdf/1906.02924)) and LCFCN([paper](https://arxiv.org/abs/1807.09856)|[code](https://github.com/ElementAI/LCFCN.git)). 
The LCFCN part contains most of the original code while the PseudoEdgeNet was implemented according to the paper as we weren't able to find existing code from the project.

## 2. Usage

### 2.1 Environment setup

To pre-setup the environment, we recommand to use 
```
conda env create -f environment.yml
```
and
```
pip install -r requirements.txt
```
This command installs pydicom and the [Haven library](https://github.com/ElementAI/haven) which helps in managing the experiments.

### 2.2 Prerequest

A stain norm process need to be done in the preprocessing step before generating objectness map.
`git clone https://github.com/wanghao14/Stain_Normalization.git`

### 2.3 Download Dataset
You can download the `BBBC`, `TNBC` or `MoNuSegTraingData` to the directory. It contains the splitted index for each validation fold and the dataset.

### 2.4 Train and cross-validation
#### 2.4.1 Preprocessing
Before training, the objectness map for each image should be generated first. Make sure the images ,point annotations and the groundtruth (for validation) saved in the path as below:
```
Dataset
└── Train
    └── Images
    └── Points
    └── GroundTruth
└── Validate
    └── ...
└── Test
    └── ...
```
Then run `generate_obj.py` to generate objectness maps.
#### 2.4.2 Example
Train and validate the model with BBBC dataset
```
python trainval.py -d BBBC -e exp_config_ponet.json -r 1
```

Train and validate the model with TNBC dataset
```
python trainval.py -d TNBC -e exp_config_ponet.json -r 1
```

Train and validate the pseudoedgenet model with MoNuSeg dataset
```
python trainval.py -d MoNuSegTrainingData -e exp_config_ponet.json -r 1
```

Both [`trainval.py`](trainval.py) and [`trainval.ipynb`](trainval.ipynb) can be used to train and validate the model. In [`trainval.ipynb`](trainval.ipynb), you can get a brief introduction of the setup of the dataset.

#### 2.4.3 Module location

The models are defined in [`src/models`](src/models). The dataset is defined in [`src/datasets/__init__.py`](src/datasets/__init__.py). The loss function is defined in [`lcfcn/lcfcn_loss.py`](lcfcn/lcfcn_loss.py). The function `compute_weighted_crossentropy` is used in the PseudoEdgeNet model. The `compute_lcfcn_loss` is used in LCFCN model.

Point-Supervised-Segmentation

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README