From 4feba3114fdd8d9685f8fa785e8a60718559672f Mon Sep 17 00:00:00 2001 From: Sizheng Guo <745134809@qq.com> Date: Tue, 20 Jun 2023 11:39:22 +0800 Subject: [PATCH] [Project] Medical semantic seg dataset: bccs (#2861) --- .../breastCancerCellSegmentation/README.md | 123 ++++++++++++++++++ .../breastCancerCellSegmentation_512x512.py | 42 ++++++ ...0k_breastCancerCellSegmentation-512x512.py | 18 +++ ...0k_breastCancerCellSegmentation-512x512.py | 18 +++ ...0k_breastCancerCellSegmentation-512x512.py | 18 +++ .../breastCancerCellSegmentation_dataset.py | 30 +++++ .../tools/prepare_dataset.py | 36 +++++ 7 files changed, 285 insertions(+) create mode 100644 projects/medical/2d_image/histopathology/breastCancerCellSegmentation/README.md create mode 100644 projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/breastCancerCellSegmentation_512x512.py create mode 100644 projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_breastCancerCellSegmentation-512x512.py create mode 100644 projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_breastCancerCellSegmentation-512x512.py create mode 100644 projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_breastCancerCellSegmentation-512x512.py create mode 100644 projects/medical/2d_image/histopathology/breastCancerCellSegmentation/datasets/breastCancerCellSegmentation_dataset.py create mode 100644 projects/medical/2d_image/histopathology/breastCancerCellSegmentation/tools/prepare_dataset.py diff --git a/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/README.md b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/README.md new file mode 100644 index 0000000000..97c4a0f0e5 --- /dev/null +++ b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/README.md @@ -0,0 +1,123 @@ +# breastCancerCellSegmentation + +## Description + +This project supports **`breastCancerCellSegmentation`**, which can be downloaded from [here](https://www.heywhale.com/mw/dataset/5e9e9b35ebb37f002c625423). + +### Dataset Overview + +This dataset, with 58 H&E-stained histopathology images was used for breast cancer cell detection and associated real-world data. +Conventional histology uses a combination of hematoxylin and eosin stains, commonly referred to as H&E. These images are stained because most cells are inherently transparent with little or no intrinsic pigment. +Certain special stains selectively bind to specific components and can be used to identify biological structures such as cells. + +### Original Statistic Information + +| Dataset name | Anatomical region | Task type | Modality | Num. Classes | Train/Val/Test Images | Train/Val/Test Labeled | Release Date | License | +| -------------------------------------------------------------------------------------------- | ----------------- | ------------ | -------------- | ------------ | --------------------- | ---------------------- | ------------ | --------------------------------------------------------------- | +| [breastCancerCellSegmentation](https://www.heywhale.com/mw/dataset/5e9e9b35ebb37f002c625423) | cell | segmentation | histopathology | 2 | 58/-/- | yes/-/- | 2020 | [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 | 58 | 98.37 | - | - | - | - | +| breastCancerCell | 58 | 1.63 | - | - | - | - | + +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/histopathology/breastCancerCellSegmentation/breastCancerCellSegmentation_dataset.png) + +## Usage + +### 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) v1.0.0 + +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 `breastCancerCellSegmentation/` root directory, run the following line to add the current directory to `PYTHONPATH`: + +```shell +export PYTHONPATH=`pwd`:$PYTHONPATH +``` + +### Dataset Preparing + +- Download dataset from [here](https://www.heywhale.com/mw/dataset/5e9e9b35ebb37f002c625423) and save it to the `data/` directory . +- Decompress data to path `data/`. This will create a new folder named `data/breastCancerCellSegmentation/`, which contains the original image data. +- run script `python tools/prepare_dataset.py` to format data and change folder structure as below. + +```none + mmsegmentation + ├── mmseg + ├── projects + │ ├── medical + │ │ ├── 2d_image + │ │ │ ├── histopathology + │ │ │ │ ├── breastCancerCellSegmentation + │ │ │ │ │ ├── configs + │ │ │ │ │ ├── datasets + │ │ │ │ │ ├── tools + │ │ │ │ │ ├── data + │ │ │ │ │ │ ├── breastCancerCellSegmentation + | │ │ │ │ │ │ ├── train.txt + | │ │ │ │ │ │ ├── val.txt + | │ │ │ │ │ │ ├── images + | │ │ │ │ │ │ | ├── xxx.tif + | │ │ │ │ │ │ ├── masks + | │ │ │ │ │ │ | ├── xxx.TIF + +``` + +### Training commands + +Train models on a single server with one GPU. + +```shell +mim train mmseg ./configs/${CONFIG_FILE} +``` + +### Testing commands + +Test models on a single server with one GPU. + +```shell +mim test mmseg ./configs/${CONFIG_FILE} --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 + + - [x] 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. diff --git a/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/breastCancerCellSegmentation_512x512.py b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/breastCancerCellSegmentation_512x512.py new file mode 100644 index 0000000000..1cf0fccf5b --- /dev/null +++ b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/breastCancerCellSegmentation_512x512.py @@ -0,0 +1,42 @@ +dataset_type = 'breastCancerCellSegmentationDataset' +data_root = 'data/breastCancerCellSegmentation' +img_scale = (512, 512) +train_pipeline = [ + dict(type='LoadImageFromFile', imdecode_backend='tifffile'), + dict(type='LoadAnnotations', imdecode_backend='tifffile'), + 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', imdecode_backend='tifffile'), + dict(type='Resize', scale=img_scale, keep_ratio=False), + dict(type='LoadAnnotations', imdecode_backend='tifffile'), + 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']) diff --git a/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_breastCancerCellSegmentation-512x512.py b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_breastCancerCellSegmentation-512x512.py new file mode 100644 index 0000000000..55d1708968 --- /dev/null +++ b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_breastCancerCellSegmentation-512x512.py @@ -0,0 +1,18 @@ +_base_ = [ + 'mmseg::_base_/models/fcn_unet_s5-d16.py', + './breastCancerCellSegmentation_512x512.py', + 'mmseg::_base_/default_runtime.py', + 'mmseg::_base_/schedules/schedule_20k.py' +] +custom_imports = dict(imports='datasets.breastCancerCellSegmentation_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) diff --git a/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_breastCancerCellSegmentation-512x512.py b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_breastCancerCellSegmentation-512x512.py new file mode 100644 index 0000000000..cf28aad739 --- /dev/null +++ b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_breastCancerCellSegmentation-512x512.py @@ -0,0 +1,18 @@ +_base_ = [ + 'mmseg::_base_/models/fcn_unet_s5-d16.py', + './breastCancerCellSegmentation_512x512.py', + 'mmseg::_base_/default_runtime.py', + 'mmseg::_base_/schedules/schedule_20k.py' +] +custom_imports = dict(imports='datasets.breastCancerCellSegmentation_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) diff --git a/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_breastCancerCellSegmentation-512x512.py b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_breastCancerCellSegmentation-512x512.py new file mode 100644 index 0000000000..29aaff3894 --- /dev/null +++ b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_breastCancerCellSegmentation-512x512.py @@ -0,0 +1,18 @@ +_base_ = [ + 'mmseg::_base_/models/fcn_unet_s5-d16.py', + './breastCancerCellSegmentation_512x512.py', + 'mmseg::_base_/default_runtime.py', + 'mmseg::_base_/schedules/schedule_20k.py' +] +custom_imports = dict(imports='datasets.breastCancerCellSegmentation_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) diff --git a/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/datasets/breastCancerCellSegmentation_dataset.py b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/datasets/breastCancerCellSegmentation_dataset.py new file mode 100644 index 0000000000..eeceb6318c --- /dev/null +++ b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/datasets/breastCancerCellSegmentation_dataset.py @@ -0,0 +1,30 @@ +from mmseg.datasets import BaseSegDataset +from mmseg.registry import DATASETS + + +@DATASETS.register_module() +class breastCancerCellSegmentationDataset(BaseSegDataset): + """breastCancerCellSegmentationDataset dataset. + + In segmentation map annotation for breastCancerCellSegmentationDataset, + ``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', 'breastCancerCell')) + + def __init__(self, + img_suffix='_ccd.tif', + seg_map_suffix='.TIF', + 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) diff --git a/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/tools/prepare_dataset.py b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/tools/prepare_dataset.py new file mode 100644 index 0000000000..09cc689c86 --- /dev/null +++ b/projects/medical/2d_image/histopathology/breastCancerCellSegmentation/tools/prepare_dataset.py @@ -0,0 +1,36 @@ +import argparse +import glob +import os + +from sklearn.model_selection import train_test_split + + +def save_anno(img_list, file_path, suffix): + # 只保留文件名，不保留后缀 + img_list = [x.split('/')[-1][:-len(suffix)] for x in img_list] + + with open(file_path, 'w') as file_: + for x in list(img_list): + file_.write(x + '\n') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + '--data_root', default='data/breastCancerCellSegmentation/') + args = parser.parse_args() + data_root = args.data_root + + # 1. 划分训练集、验证集 + # 1.1 获取所有图片路径 + img_list = glob.glob(os.path.join(data_root, 'images', '*.tif')) + img_list.sort() + mask_list = glob.glob(os.path.join(data_root, 'masks', '*.TIF')) + mask_list.sort() + assert len(img_list) == len(mask_list) + # 1.2 划分训练集、验证集、测试集 + train_img_list, val_img_list, train_mask_list, val_mask_list = train_test_split( # noqa + img_list, mask_list, test_size=0.2, random_state=42) + # 1.3 保存划分结果 + save_anno(train_img_list, os.path.join(data_root, 'train.txt'), '_ccd.tif') + save_anno(val_img_list, os.path.join(data_root, 'val.txt'), '_ccd.tif')