[Project] add Vampire dataset project (#2633)

2023-05-06 17:39:12 +08:00 · 2023-05-06 17:39:12 +08:00 · 77836e6231
parent 65b63cca99
commit 77836e6231
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--- a/projects/medical/2d_image/fluorescein_angriogram/vampire/README.md
+++ b/projects/medical/2d_image/fluorescein_angriogram/vampire/README.md
@ -0,0 +1,158 @@
+# 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.
--- a/projects/medical/2d_image/fluorescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_vampire-512x512.py
+++ b/projects/medical/2d_image/fluorescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_vampire-512x512.py
@ -0,0 +1,19 @@
+_base_ = [
+    'mmseg::_base_/models/fcn_unet_s5-d16.py', './vampire_512x512.py',
+    'mmseg::_base_/default_runtime.py',
+    'mmseg::_base_/schedules/schedule_20k.py'
+]
+custom_imports = dict(imports='datasets.vampire_dataset')
+img_scale = (512, 512)
+data_preprocessor = dict(size=img_scale)
+optimizer = dict(lr=0.0001)
+optim_wrapper = dict(optimizer=optimizer)
+model = dict(
+    type='EncoderDecoder',
+    data_preprocessor=data_preprocessor,
+    pretrained=None,
+    decode_head=dict(num_classes=2),
+    auxiliary_head=None,
+    test_cfg=dict(mode='whole', _delete_=True))
+vis_backends = None
+visualizer = dict(vis_backends=vis_backends)
--- a/projects/medical/2d_image/fluorescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_vampire-512x512.py
+++ b/projects/medical/2d_image/fluorescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_vampire-512x512.py
@ -0,0 +1,19 @@
+_base_ = [
+    'mmseg::_base_/models/fcn_unet_s5-d16.py', './vampire_512x512.py',
+    'mmseg::_base_/default_runtime.py',
+    'mmseg::_base_/schedules/schedule_20k.py'
+]
+custom_imports = dict(imports='datasets.vampire_dataset')
+img_scale = (512, 512)
+data_preprocessor = dict(size=img_scale)
+optimizer = dict(lr=0.001)
+optim_wrapper = dict(optimizer=optimizer)
+model = dict(
+    type='EncoderDecoder',
+    data_preprocessor=dict(size=img_scale),
+    pretrained=None,
+    decode_head=dict(num_classes=2),
+    auxiliary_head=None,
+    test_cfg=dict(mode='whole', _delete_=True))
+vis_backends = None
+visualizer = dict(vis_backends=vis_backends)
--- a/projects/medical/2d_image/fluorescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_vampire-512x512.py
+++ b/projects/medical/2d_image/fluorescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_vampire-512x512.py
@ -0,0 +1,22 @@
+_base_ = [
+    'mmseg::_base_/models/fcn_unet_s5-d16.py', './vampire_512x512.py',
+    'mmseg::_base_/default_runtime.py',
+    'mmseg::_base_/schedules/schedule_20k.py'
+]
+custom_imports = dict(imports='datasets.vampire_dataset')
+img_scale = (512, 512)
+data_preprocessor = dict(size=img_scale)
+optimizer = dict(lr=0.01)
+optim_wrapper = dict(optimizer=optimizer)
+model = dict(
+    type='EncoderDecoder',
+    data_preprocessor=data_preprocessor,
+    pretrained=None,
+    decode_head=dict(
+        num_classes=2,
+        loss_decode=dict(type='CrossEntropyLoss', use_sigmoid=True),
+        out_channels=1),
+    auxiliary_head=None,
+    test_cfg=dict(mode='whole', _delete_=True))
+vis_backends = None
+visualizer = dict(vis_backends=vis_backends)
--- a/projects/medical/2d_image/fluorescein_angriogram/vampire/configs/vampire_512x512.py
+++ b/projects/medical/2d_image/fluorescein_angriogram/vampire/configs/vampire_512x512.py
@ -0,0 +1,42 @@
+dataset_type = 'VampireDataset'
+data_root = 'data'
+img_scale = (512, 512)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', scale=img_scale, keep_ratio=False),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='PackSegInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=img_scale, keep_ratio=False),
+    dict(type='LoadAnnotations'),
+    dict(type='PackSegInputs')
+]
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='InfiniteSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='train.txt',
+        data_prefix=dict(img_path='images/', seg_map_path='masks/'),
+        pipeline=train_pipeline))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='val.txt',
+        data_prefix=dict(img_path='images/', seg_map_path='masks/'),
+        pipeline=test_pipeline))
+test_dataloader = val_dataloader
+val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mDice'])
+test_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mDice'])
--- a/projects/medical/2d_image/fluorescein_angriogram/vampire/datasets/init.py
+++ b/projects/medical/2d_image/fluorescein_angriogram/vampire/datasets/init.py
@ -0,0 +1,3 @@
+from .vampire_dataset import VampireDataset
+
+__all__ = ['VampireDataset']
--- a/projects/medical/2d_image/fluorescein_angriogram/vampire/datasets/vampire_dataset.py
+++ b/projects/medical/2d_image/fluorescein_angriogram/vampire/datasets/vampire_dataset.py
@ -0,0 +1,28 @@
+from mmseg.datasets import BaseSegDataset
+from mmseg.registry import DATASETS
+
+
+@DATASETS.register_module()
+class VampireDataset(BaseSegDataset):
+    """VampireDataset dataset.
+
+    In segmentation map annotation for VampireDataset, 0 stands for background,
+    which is included in 2 categories. ``reduce_zero_label`` is fixed to
+    False. The ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is
+    fixed to '.png'.
+    Args:
+        img_suffix (str): Suffix of images. Default: '.png'
+        seg_map_suffix (str): Suffix of segmentation maps. Default: '.png'
+    """
+    METAINFO = dict(classes=('background', 'vessel'))
+
+    def __init__(self,
+                 img_suffix='.png',
+                 seg_map_suffix='.png',
+                 reduce_zero_label=False,
+                 **kwargs) -> None:
+        super().__init__(
+            img_suffix=img_suffix,
+            seg_map_suffix=seg_map_suffix,
+            reduce_zero_label=reduce_zero_label,
+            **kwargs)
--- a/projects/medical/2d_image/fluorescein_angriogram/vampire/tools/prepare_dataset.py
+++ b/projects/medical/2d_image/fluorescein_angriogram/vampire/tools/prepare_dataset.py
@ -0,0 +1,44 @@
+import os
+import shutil
+
+from PIL import Image
+
+path = 'data'
+
+if not os.path.exists(os.path.join(path, 'images', 'train')):
+    os.system(f'mkdir -p {os.path.join(path, "images", "train")}')
+
+if not os.path.exists(os.path.join(path, 'masks', 'train')):
+    os.system(f'mkdir -p {os.path.join(path, "masks", "train")}')
+
+origin_data_path = os.path.join(path, 'vesselSegmentation')
+
+imgs_amd14 = os.listdir(os.path.join(origin_data_path, 'AMD14'))
+imgs_ger7 = os.listdir(os.path.join(origin_data_path, 'GER7'))
+
+for img in imgs_amd14:
+    shutil.copy(
+        os.path.join(origin_data_path, 'AMD14', img),
+        os.path.join(path, 'images', 'train', img))
+    # copy GT
+    img_gt = img.replace('.png', '-GT.png')
+    shutil.copy(
+        os.path.join(origin_data_path, 'AMD14-GT', f'{img_gt}'),
+        os.path.join(path, 'masks', 'train', img))
+
+for img in imgs_ger7:
+    shutil.copy(
+        os.path.join(origin_data_path, 'GER7', img),
+        os.path.join(path, 'images', 'train', img))
+    # copy GT
+    img_gt = img.replace('.bmp', '-GT.png')
+    img = img.replace('bmp', 'png')
+    shutil.copy(
+        os.path.join(origin_data_path, 'GER7-GT', img_gt),
+        os.path.join(path, 'masks', 'train', img))
+
+imgs = os.listdir(os.path.join(path, 'images', 'train'))
+for img in imgs:
+    if not img.endswith('.png'):
+        im = Image.open(os.path.join(path, 'images', 'train', img))
+        im.save(os.path.join(path, 'images', 'train', img[:-4] + '.png'))