# Vessel Assessment and Measurement Platform for Images of the REtina

## Description

This project support **`Vessel Assessment and Measurement Platform for Images of the REtina`**, and the dataset used in this project can be downloaded from [here](https://vampire.computing.dundee.ac.uk/vesselseg.html).

### Dataset Overview

In order to promote evaluation of vessel segmentation on ultra-wide field-of-view (UWFV) fluorescein angriogram (FA) frames, we make public 8 frames from two different sequences, the manually annotated images and the result of our automatic vessel segmentation algorithm.

### Original Statistic Information

| Dataset name                                                     | Anatomical region | Task type    | Modality               | Num. Classes | Train/Val/Test Images | Train/Val/Test Labeled | Release Date | License                                                         |
| ---------------------------------------------------------------- | ----------------- | ------------ | ---------------------- | ------------ | --------------------- | ---------------------- | ------------ | --------------------------------------------------------------- |
| [Vampire](https://vampire.computing.dundee.ac.uk/vesselseg.html) | vessel            | segmentation | fluorescein angriogram | 2            | 8/-/-                 | yes/-/-                | 2017         | [CC-BY-NC 4.0](https://creativecommons.org/licenses/by-sa/4.0/) |

| Class Name | Num. Train | Pct. Train | Num. Val | Pct. Val | Num. Test | Pct. Test |
| :--------: | :--------: | :--------: | :------: | :------: | :-------: | :-------: |
| background |     8      |   96.75    |    -     |    -     |     -     |     -     |
|   vessel   |     8      |    3.25    |    -     |    -     |     -     |     -     |

Note:

- `Pct` means percentage of pixels in this category in all pixels.

### Visualization

![bac](https://raw.githubusercontent.com/uni-medical/medical-datasets-visualization/main/2d/semantic_seg/fluorescein_angriogram/vampire/vampire_dataset.png)

## Dataset Citation

```bibtex

@inproceedings{perez2011improving,
  title={Improving vessel segmentation in ultra-wide field-of-view retinal fluorescein angiograms},
  author={Perez-Rovira, Adria and Zutis, K and Hubschman, Jean Pierre and Trucco, Emanuele},
  booktitle={2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
  pages={2614--2617},
  year={2011},
  organization={IEEE}
}

@article{perez2011rerbee,
  title={RERBEE: robust efficient registration via bifurcations and elongated elements applied to retinal fluorescein angiogram sequences},
  author={Perez-Rovira, Adria and Cabido, Raul and Trucco, Emanuele and McKenna, Stephen J and Hubschman, Jean Pierre},
  journal={IEEE Transactions on Medical Imaging},
  volume={31},
  number={1},
  pages={140--150},
  year={2011},
  publisher={IEEE}
}

```

### Prerequisites

- Python v3.8
- PyTorch v1.10.0
- pillow(PIL) v9.3.0
- scikit-learn(sklearn) v1.2.0
- [MIM](https://github.com/open-mmlab/mim) v0.3.4
- [MMCV](https://github.com/open-mmlab/mmcv) v2.0.0rc4
- [MMEngine](https://github.com/open-mmlab/mmengine) v0.2.0 or higher
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation) v1.0.0rc5

All the commands below rely on the correct configuration of `PYTHONPATH`, which should point to the project's directory so that Python can locate the module files. In `vampire/` root directory, run the following line to add the current directory to `PYTHONPATH`:

```shell
export PYTHONPATH=`pwd`:$PYTHONPATH
```

### Dataset preparing

- download dataset from [here](https://vampire.computing.dundee.ac.uk/vesselseg.html) and decompression data to path `'data/'`.
- run script `"python tools/prepare_dataset.py"` to split dataset and change folder structure as below.
- run script `python ../../tools/split_seg_dataset.py` to split dataset. For the Bacteria_detection dataset, as there is no test or validation dataset, we sample 20% samples from the whole dataset as the validation dataset and 80% samples for training data and make two filename lists `train.txt` and `val.txt`. As we set the random seed as the hard code, we eliminated the randomness, the dataset split actually can be reproducible.

```none
  mmsegmentation
  ├── mmseg
  ├── projects
  │   ├── medical
  │   │   ├── 2d_image
  │   │   │   ├── fluorescein_angriogram
  │   │   │   │   ├── vampire
  │   │   │   │   │   ├── configs
  │   │   │   │   │   ├── datasets
  │   │   │   │   │   ├── tools
  │   │   │   │   │   ├── data
  │   │   │   │   │   │   ├── train.txt
  │   │   │   │   │   │   ├── val.txt
  │   │   │   │   │   │   ├── images
  │   │   │   │   │   │   │   ├── train
  │   │   │   │   |   │   │   │   ├── xxx.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── xxx.png
  │   │   │   │   │   │   ├── masks
  │   │   │   │   │   │   │   ├── train
  │   │   │   │   |   │   │   │   ├── xxx.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── xxx.png
```

### Divided Dataset Information

***Note: The table information below is divided by ourselves.***

| Class Name | Num. Train | Pct. Train | Num. Val | Pct. Val | Num. Test | Pct. Test |
| :--------: | :--------: | :--------: | :------: | :------: | :-------: | :-------: |
| background |     6      |   97.48    |    2     |  94.54   |     -     |     -     |
|   vessel   |     6      |    2.52    |    2     |   5.46   |     -     |     -     |

### Training commands

To train models on a single server with one GPU. (default）

```shell
mim train mmseg ./configs/${CONFIG_PATH}
```

### Testing commands

To test models on a single server with one GPU. (default)

```shell
mim test mmseg ./configs/${CONFIG_PATH}  --checkpoint ${CHECKPOINT_PATH}
```

## Checklist

- [x] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.

  - [x] Finish the code

  - [x] Basic docstrings & proper citation

  - [ ] Test-time correctness

  - [x] A full README

- [ ] Milestone 2: Indicates a successful model implementation.

  - [ ] Training-time correctness

- [ ] Milestone 3: Good to be a part of our core package!

  - [ ] Type hints and docstrings

  - [ ] Unit tests

  - [ ] Code polishing

  - [ ] Metafile.yml

- [ ] Move your modules into the core package following the codebase's file hierarchy structure.

- [ ] Refactor your modules into the core package following the codebase's file hierarchy structure.