File size: 1,807 Bytes
a19d827 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
# Vertebra-Focused-Landmark-Detection-Pytorch
Vertebra-Focused Landmark Detection for Scoliosis Assessment [[arXiv](https://arxiv.org/pdf/2001.03187.pdf)]
Accepted to ISBI2020.
Please cite the article in your publications if it helps your research:
@article{yi2020vertebra,
title={Vertebra-Focused Landmark Detection for Scoliosis Assessment},
author={Yi, Jingru and Wu, Pengxiang and Huang, Qiaoying and Qu, Hui and Metaxas, Dimitris N},
booktitle={ISBI},
year={2020}
}
<p align="center">
<img src="imgs/pic1.png", width="400">
</p>
<p align="center">
<img src="imgs/pic2.png", width="800">
</p>
# Dependencies
Ubuntu 14.04, Python 3.6.4, PyTorch 1.1.0, OpenCV-Python 4.1.0.25
# How to start
## Prepare Dataset
To directly use dataset.py, you can arrange the dataset as follows:
```
/dataPath/data
/train/*.jpg
/val/*.jpg
/test/*.jpg
/dataPath/labels/
/train/*.mat
/val/*.mat
/test/*.mat
```
The source dataset is from [[dataset16](http://spineweb.digitalimaginggroup.ca/spineweb/index.php?n=Main.Datasets#Dataset_16.3A_609_spinal_anterior-posterior_x-ray_images)].
To adapt the code to your own dataset, you can modify the dataset.py, for example, change the 'load_gt_pts' function to adapt it to your own annotations. The pretrained weights can be downloaded [here](https://drive.google.com/drive/folders/1LhKnGVE8dUw0nK9_x4vPNY_L7sPY2_aQ?usp=sharing).
## Train the model
```ruby
python main.py --data_dir dataPath --epochs 50 --batch_size 2 --dataset spinal --phase train
```
## Test the model
```ruby
python main.py --resume weightPath --data_dir dataPath --dataset spinal --phase test
```
## Evaluate the model
```ruby
python main.py --resume weightPath --data_dir dataPath --dataset spinal --phase eval
|