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Tong Zhang 943faed604 | 1 year ago | |
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scripts | 2 years ago | |
src | 2 years ago | |
README.md | 1 year ago | |
ellipsenet.ckpt | 2 years ago | |
eval.py | 2 years ago | |
eval_log.txt | 2 years ago | |
requirements.txt | 2 years ago | |
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train.py | 2 years ago | |
train_log.txt | 2 years ago |
Paper: Chen, J., Zhang, Y., Wang, J., Zhou, X., He, Y. and Zhang, T., 2021, September. EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 218-227). Springer, Cham.
Please be noted that this is not a MindSpore version of EllipseNet, but using a 2D Unet to train a segmentation network and then using ellipses to fit the segmentation results. The details of this method can be found in ref[6] as compared in our paper. The PyTorch version of this algorithm can be found in https://git.openi.org.cn/OpenMedIA/EllipseNet
The EllipseNet segementation network takes n 2D volumetric images as input, applies input and feature transformations. BN is introdued before each ReLU.
Fetal Four Chamber View Ultrasound Scans
suwen package
pip install -r requirements.txt
pip install ./suwen-1.0.1-py3-none-any.whl
After installing MindSpore via the official website, you can start training and evaluation as follows:
# enter script dir, train PointNet
sh run_train_ascend.sh
# enter script dir, evaluate PointNet
sh run_eval.sh
.
├── README.md
├── ellipsenet.ckpt
├── eval.py
├── eval_log.txt
├── requirements.txt
├── scripts
│ ├── run_eval.sh
│ ├── run_train_ascend.sh
│ └── scripts
│ ├── run_eval.sh
│ └── run_train_ascend.sh
├── src
│ ├── __init__.py
│ ├── __pycache__
│ │ ├── cross_entropy_with_logits.cpython-37.pyc
│ │ ├── dataset.cpython-37.pyc
│ │ ├── dice_loss.cpython-37.pyc
│ │ └── dice_metric.cpython-37.pyc
│ ├── config.py
│ ├── convert_nifti.py
│ ├── cross_entropy_with_logits.py
│ ├── dataset.py
│ ├── dice_loss.py
│ ├── dice_metric.py
│ ├── loss.py
│ ├── lr_schedule.py
│ ├── transform.py
│ └── utils.py
├── suwen-1.0.1-py3-none-any.whl
├── train.py
└── train_log.txt
4 directories, 27 files
Major parameters in train.py are as follows:
--data_path: The absolute full path to the train and evaluation datasets.
--ckpt_path: The absolute full path to the checkpoint file saved after training.
More hyperparamteters can be modified in src/config.py.
running on Ascend
sh run_train_ascend.sh
After training, the loss value will be achieved as what in train_log.txt
The model checkpoint will be saved in the current ckpt directory.
Before running the command below, please check the checkpoint path used for evaluation.
running on Ascend
sh scripts/run_eval.sh
You can view the results through the file "eval_log". The accuracy of the test dataset will be as what in eval_log.txt.
Parameters | |
---|---|
Resource | Ascend 910; CPU 2.60GHz, 24cores; Memory, 96G |
uploaded Date | 11/29/2021 (month/day/year) |
MindSpore Version | 1.3.0 |
Dataset | Fetal Four Chamber View |
Training Parameters | epoch=100 |
Optimizer | Adam |
Loss Function | Softmax Cross Entropy |
outputs | probability |
Loss | Dice Loss |
Checkpoint for Fine tuning | 23M (ckpt file) |
Parameters | |
---|---|
Resource | Ascend 910; CPU 2.60GHz, 24cores; Memory, 96G |
uploaded Date | 11/29/2021 (month/day/year) |
MindSpore Version | 1.3.0 |
Dataset | Fetal Four Chamber View |
batch_size | 1 |
outputs | probability |
Dice | 91.72% |
If you find this project useful for your research, please cit our work use the following BibTeX entry.
@inproceedings{chen2021ellipsenet,
title={Ellipsenet: Anchor-free ellipse detection for automatic cardiac biometrics in fetal echocardiography},
author={Chen, Jiancong and Zhang, Yingying and Wang, Jingyi and Zhou, Xiaoxue and He, Yihua and Zhang, Tong},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={218--227},
year={2021},
organization={Springer}
}
If you have any questions about this paper, welcome to email to zhangt02@pcl.ac.cn
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