Content

• 2DUNet Descriptions
• Model Architecture
• Dataset
• Environment Requirements
• Quick Start
• Script Description
• Script Parameters
• Training Process
• Evaluation Process
  • Evaluation
• Model Description
  • Performance
    • Training Performance
    • Inference Performance

2DUNet Descriptions

2DUNet was proposed in 2015, it is a typoe of neural network that directly consumes 2D images. The u-net archiecture ahiceves very good performance on very different biomedical segmentation applications. Without strong data augmentations, it only needs very few annotated images and has a ver reasonable trainning time.

Paper: Ronneberger, O., Fischer, P. and Brox, T., 2015, October. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.

Paper: Hofmanninger J, Prayer F, Pan J, Röhrich S, Prosch H, Langs G. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. European Radiology Experimental. 2020 Dec;4(1):1-3.

Model Architecture

The 2DUNet segementation network takes n 2D images as input, applies input and feature transformations. BN is introdued before each ReLU.

Dataset

2d-Covid-segmentaiton

Dataset size: 1.2G

• Train: 60M, 1713 images and corresponding labels.
• Test: 15M, 429 images and corresponding labels.

Environment Requirements

• Hardware（Ascend）
  • Prepare hardware environment with Ascend processor.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore Tutorials
  • MindSpore Python API

• suwen package

  pip install -r requirements.txt
  pip install ./suwen-1.0.1-py3-none-any.whl

Quick Start

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

Script Description

.
├── README.md
├── UNet2d.ckpt
├── eval.py
├── eval_log.txt
├── imgs
│   └── 2dUNet.png
├── requirements.txt
├── scripts
│   ├── run_eval.sh
│   └── run_train_ascend.sh
├── src
│   ├── __pycache__
│   │   ├── cross_entropy_with_logits.cpython-37.pyc
│   │   ├── dataset.cpython-37.pyc
│   │   ├── dice_loss.cpython-37.pyc
│   │   └── dice_metric.cpython-37.pyc
│   ├── cross_entropy_with_logits.py
│   ├── dataset.py
│   ├── dice_loss.py
│   └── dice_metric.py
├── suwen-1.0.1-py3-none-any.whl
├── train.py
└── train_log.txt

4 directories, 19 files

Script Parameters

Major parameters in train.py are as follows:

--data_path: The absolute full path to the train and evaluation datasets.
--seg_path : The absolute full path to the train and evaluation segmentation labels.
--ckpt_path: The absolute full path to the checkpoint file saved after training.

More hyperparamteters can be modified in src/config.py.

Training Process

• 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.

Evaluation Process

Evaluation

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.

Model Description

Performance

Training Performance

Parameters
Resource	Ascend 910; CPU 2.60GHz, 24cores; Memory, 96G
uploaded Date	11/22/2021 (month/day/year)
MindSpore Version	1.3.0
Dataset	MM-WHS
Training Parameters	epoch=600
Optimizer	Adam
Loss Function	Softmax Cross Entropy
outputs	probability
Loss	SoftmaxCrossEntropyWithLogits
Speed	-ms/step-
Total time	-ms
Checkpoint for Fine tuning	23M (.ckpt file)

Inference Performance

Parameters
Resource	Ascend 910; CPU 2.60GHz, 24cores; Memory, 96G
uploaded Date	11/22/2021 (month/day/year)
MindSpore Version	1.3.0
Dataset	MM-WHS
batch_size	1
outputs	probability
Dice	75.86%