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[Project] Medical semantic seg dataset: 2pm vessel ()

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projects/medical/2d_image/microscopy_images/2pm_vessel/README.md 100644
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# 2-PM Vessel Dataset
## Description
This project supports **`2-PM Vessel Dataset`**, which can be downloaded from [here](https://opendatalab.org.cn/2-PM_Vessel_Dataset).
### Dataset Overview
An open-source volumetric brain vasculature dataset obtained with two-photon microscopy at Focused Ultrasound Lab, at Sunnybrook Research Institute (affiliated with University of Toronto by Dr. Alison Burgess, Charissa Poon and Marc Santos).
The dataset contains a total of 12 volumetric stacks consisting images of mouse brain vasculature and tumor vasculature.
### Information Statistics
| Dataset Name | Anatomical Region | Task Type | Modality | Num. Classes | Train/Val/Test Images | Train/Val/Test Labeled | Release Date | License |
| ------------------------------------------------------------ | ----------------- | ------------ | ----------------- | ------------ | --------------------- | ---------------------- | ------------ | ------------------------------------------------------------- |
| [2pm_vessel](https://opendatalab.org.cn/2-PM_Vessel_Dataset) | vessel | segmentation | microscopy_images | 2 | 216/-/- | yes/-/- | 2021 | [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) |
| Class Name | Num. Train | Pct. Train | Num. Val | Pct. Val | Num. Test | Pct. Test |
| :--------: | :--------: | :--------: | :------: | :------: | :-------: | :-------: |
| background | 216 | 85.78 | - | - | - | - |
| vessel | 180 | 14.22 | - | - | - | - |
Note:
- `Pct` means percentage of pixels in this category in all pixels.
### Visualization
![2pmv](https://raw.githubusercontent.com/uni-medical/medical-datasets-visualization/main/2d/semantic_seg/histopathology/2pm_vessel/2pm_vessel_dataset.png?raw=true)
### Dataset Citation
```
@article{teikari2016deep,
title={Deep learning convolutional networks for multiphoton microscopy vasculature segmentation},
author={Teikari, Petteri and Santos, Marc and Poon, Charissa and Hynynen, Kullervo},
journal={arXiv preprint arXiv:1606.02382},
year={2016}
}
```
### 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 `2pm_vessel/` root directory, run the following line to add the current directory to `PYTHONPATH`:
```shell
export PYTHONPATH=`pwd`:$PYTHONPATH
```
### Dataset Preparing
- download dataset from [here](https://opendatalab.org.cn/2-PM_Vessel_Dataset) and decompress data to path `'data/'`.
- run script `"python tools/prepare_dataset.py"` to format data and change folder structure as below.
- run script `"python ../../tools/split_seg_dataset.py"` to split dataset and generate `train.txt`, `val.txt` and `test.txt`. If the label of official validation set and test set can't be obtained, we generate `train.txt` and `val.txt` from the training set randomly.
```shell
mkdir data & cd data
pip install opendatalab
odl get 2-PM_Vessel_Dataset
cd ..
python tools/prepare_dataset.py
python tools/prepare_dataset.py
```
```none
mmsegmentation
├── mmseg
├── projects
│ ├── medical
│ │ ├── 2d_image
│ │ │ ├── microscopy_images
│ │ │ │ ├── 2pm_vessel
│ │ │ │ │ ├── configs
│ │ │ │ │ ├── datasets
│ │ │ │ │ ├── tools
│ │ │ │ │ ├── data
│ │ │ │ │ │ ├── train.txt
│ │ │ │ │ │ ├── val.txt
│ │ │ │ │ │ ├── images
│ │ │ │ │ │ │ ├── train
│ │ │ │ | │ │ │ ├── xxx.png
│ │ │ │ | │ │ │ ├── ...
│ │ │ │ | │ │ │ └── xxx.png
│ │ │ │ │ │ ├── masks
│ │ │ │ │ │ │ ├── train
│ │ │ │ | │ │ │ ├── xxx.png
│ │ │ │ | │ │ │ ├── ...
│ │ │ │ | │ │ │ └── xxx.png
```
### Divided Dataset Information
***Note: The table information below is divided by ourselves.***
| Class Name | Num. Train | Pct. Train | Num. Val | Pct. Val | Num. Test | Pct. Test |
| :--------: | :--------: | :--------: | :------: | :------: | :-------: | :-------: |
| background | 172 | 85.88 | 44 | 85.4 | - | - |
| vessel | 142 | 14.12 | 38 | 14.6 | - | - |
### Training commands
To train models on a single server with one GPU. (default)
```shell
mim train mmseg ./configs/${CONFIG_FILE}
```
### Testing commands
To test models on a single server with one GPU. (default)
```shell
mim test mmseg ./configs/${CONFIG_FILE} --checkpoint ${CHECKPOINT_PATH}
```
<!-- List the results as usually done in other model's README. [Example](https://github.com/open-mmlab/mmsegmentation/tree/dev-1.x/configs/fcn#results-and-models)
You should claim whether this is based on the pre-trained weights, which are converted from the official release; or it's a reproduced result obtained from retraining the model in this project. -->
## 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.

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projects/medical/2d_image/microscopy_images/2pm_vessel/configs/2pm-vessel_512x512.py 100644
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dataset_type = 'TwoPMVesselDataset'
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'])

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projects/medical/2d_image/microscopy_images/2pm_vessel/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_2pm-vessel-512x512.py 100644
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_base_ = [
'mmseg::_base_/models/fcn_unet_s5-d16.py', './2pm-vessel_512x512.py',
'mmseg::_base_/default_runtime.py',
'mmseg::_base_/schedules/schedule_20k.py'
]
custom_imports = dict(imports='datasets.2pm-vessel_dataset')
img_scale = (512, 512)
data_preprocessor = dict(size=img_scale)
optimizer = dict(lr=0.0001)
optim_wrapper = dict(optimizer=optimizer)
model = dict(
data_preprocessor=data_preprocessor,
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)

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projects/medical/2d_image/microscopy_images/2pm_vessel/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_2pm-vessel-512x512.py 100644
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_base_ = [
'mmseg::_base_/models/fcn_unet_s5-d16.py', './2pm-vessel_512x512.py',
'mmseg::_base_/default_runtime.py',
'mmseg::_base_/schedules/schedule_20k.py'
]
custom_imports = dict(imports='datasets.2pm-vessel_dataset')
img_scale = (512, 512)
data_preprocessor = dict(size=img_scale)
optimizer = dict(lr=0.001)
optim_wrapper = dict(optimizer=optimizer)
model = dict(
data_preprocessor=data_preprocessor,
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)

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projects/medical/2d_image/microscopy_images/2pm_vessel/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_2pm-vessel-512x512.py 100644
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_base_ = [
'mmseg::_base_/models/fcn_unet_s5-d16.py', './2pm-vessel_512x512.py',
'mmseg::_base_/default_runtime.py',
'mmseg::_base_/schedules/schedule_20k.py'
]
custom_imports = dict(imports='datasets.2pm-vessel_dataset')
img_scale = (512, 512)
data_preprocessor = dict(size=img_scale)
optimizer = dict(lr=0.01)
optim_wrapper = dict(optimizer=optimizer)
model = dict(
data_preprocessor=data_preprocessor,
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)

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projects/medical/2d_image/microscopy_images/2pm_vessel/configs/fcn-unet-s5-d16_unet_1xb16-0.01lr-sigmoid-20k_bactteria-detection-512x512.py 100644
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_base_ = [
'mmseg::_base_/models/fcn_unet_s5-d16.py', './2pm-vessel_512x512.py',
'mmseg::_base_/default_runtime.py',
'mmseg::_base_/schedules/schedule_20k.py'
]
custom_imports = dict(imports='datasets.2pm-vessel_dataset')
img_scale = (512, 512)
data_preprocessor = dict(size=img_scale)
optimizer = dict(lr=0.01)
optim_wrapper = dict(optimizer=optimizer)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(
num_classes=2, loss_decode=dict(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)

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projects/medical/2d_image/microscopy_images/2pm_vessel/datasets/2pm-vessel_dataset.py 100644
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from mmseg.datasets import BaseSegDataset
from mmseg.registry import DATASETS
@DATASETS.register_module()
class TwoPMVesselDataset(BaseSegDataset):
"""TwoPMVesselDataset dataset.
In segmentation map annotation for TwoPMVesselDataset,
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'
reduce_zero_label (bool): Whether to mark label zero as ignored.
Default to False.
"""
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)

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projects/medical/2d_image/microscopy_images/2pm_vessel/tools/prepare_dataset.py 100644
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import os
import tifffile as tiff
from PIL import Image
root_path = 'data/'
image_dir = os.path.join(root_path,
'2-PM_Vessel_Dataset/raw/vesselNN_dataset/denoised')
label_dir = os.path.join(root_path,
'2-PM_Vessel_Dataset/raw/vesselNN_dataset/labels')
tgt_img_train_dir = os.path.join(root_path, 'images/train/')
tgt_mask_train_dir = os.path.join(root_path, 'masks/train/')
os.system('mkdir -p ' + tgt_img_train_dir)
os.system('mkdir -p ' + tgt_mask_train_dir)
def filter_suffix(src_dir, suffix):
suffix = '.' + suffix if '.' not in suffix else suffix
file_names = [_ for _ in os.listdir(src_dir) if _.endswith(suffix)]
file_paths = [os.path.join(src_dir, _) for _ in file_names]
return sorted(file_paths), sorted(file_names)
if __name__ == '__main__':
image_path_list, _ = filter_suffix(image_dir, suffix='tif')
label_path_list, _ = filter_suffix(label_dir, suffix='.tif')
for img_path, label_path in zip(image_path_list, label_path_list):
labels = tiff.imread(label_path)
images = tiff.imread(img_path)
assert labels.ndim == 3
assert images.shape == labels.shape
name = img_path.split('/')[-1].replace('.tif', '')
# a single .tif file contains multiple slices
# as long as it is read by tifffile package.
for i in range(labels.shape[0]):
slice_name = name + '_' + str(i).rjust(3, '0') + '.png'
image = images[i]
label = labels[i] // 255
save_path_label = os.path.join(tgt_mask_train_dir, slice_name)
Image.fromarray(label).save(save_path_label)
save_path_image = os.path.join(tgt_img_train_dir, slice_name)
Image.fromarray(image).convert('RGB').save(save_path_image)