[Project] Medical semantic seg dataset: 2pm vessel (#2685)

projects/medical/2d_image/microscopy_images/2pm_vessel/README.md

@@ -0,0 +1,153 @@
+# 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.

projects/medical/2d_image/microscopy_images/2pm_vessel/configs/2pm-vessel_512x512.py

@@ -0,0 +1,42 @@
+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'])

projects/medical/2d_image/microscopy_images/2pm_vessel/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_2pm-vessel-512x512.py

@@ -0,0 +1,17 @@
+_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)

projects/medical/2d_image/microscopy_images/2pm_vessel/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_2pm-vessel-512x512.py

@@ -0,0 +1,17 @@
+_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)

projects/medical/2d_image/microscopy_images/2pm_vessel/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_2pm-vessel-512x512.py

@@ -0,0 +1,17 @@
+_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)

projects/medical/2d_image/microscopy_images/2pm_vessel/configs/fcn-unet-s5-d16_unet_1xb16-0.01lr-sigmoid-20k_bactteria-detection-512x512.py

@@ -0,0 +1,18 @@
+_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)

projects/medical/2d_image/microscopy_images/2pm_vessel/datasets/2pm-vessel_dataset.py

@@ -0,0 +1,31 @@
+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)

projects/medical/2d_image/microscopy_images/2pm_vessel/tools/prepare_dataset.py

@@ -0,0 +1,46 @@
+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)