MNet_DeepCDR - GitLite

KO | EN

gitlite — search

#javascript #hacktoberfest #react #ai #android #deep-learning #docker #nodejs #java #awesome #awesome-list #security #pytorch #open-source

MNet_DeepCDR

(★ 173)

Code for TMI 2018 "Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation"

파일 탐색기

최종 버전 다운로드 (.zip)

- - Demo_Full-checkpoint.ipynb
  - Demo_ipython-checkpoint.ipynb
- - logging.yml
- - Model_DiscSeg_ORIGA.h5
  - Model_MNet_REFUGE.h5
- - REFUGE_result.zip
  - ROC_curve.png
- - .gitkeep
- - CS50041_R.jpg
  - CS50106_R.jpg
- __init__.py
- mat_scr.zip
- mnet_utils.py
- Model_DiscSeg.py
- Model_MNet.py
- Step_1_Disc_Crop.py
- Step_2_MNet_train.py
- Step_3_MNet_test.py
- Step_4_CDR_output.m
.editorconfig
.gitignore
README.md
requirements.txt
setup.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/HzFu/MNet_DeepCDR

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd MNet_DeepCDR

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. 공식 설치 스크립트

쉬움 추천

사전 준비물

Python 3 pip 명령어를 쓰려면 Python이 필요합니다.

pip install .

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

pip install .

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

mnet_deep_cdr
=============
![Python version range](https://img.shields.io/badge/python-2.7%E2%80%933.6+-blue.svg)
**Code for TMI 2018 "Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation"**

Project homepage：http://hzfu.github.io/proj_glaucoma_fundus.html

## Install dependencies

pip install -r requirements.txt

## Install package

pip install .

OpenCV will need to be installed separately.

---

1. The code is based on: *TensorFlow 1.14 (with Keras) + Matlab*
2. The deep output is raw segmentation result without ellipse fitting. The Matlab code is the ellipse fitting and CDR calculation (by using PDollar toolbox: https://pdollar.github.io/toolbox/).
3. You can run the 'Step\_3\_MNet\_test.py' for testing any new image directly.
4. We also provided the validation and test results on [REFUGE dataset](https://refuge.grand-challenge.org/home/) in 'REFUGE\_result' fold.
5. **Note: Due to the 'scipy.misc.imresize' in SciPy 1.0.0 has been removed in SciPy 1.3.0, the original trained model 'Model\_MNet\_REFUGE.h5' is not suitable. If you want to segment disc/cup from fundus image, you can consider our newest methods: CE-Net and AG-Net, which obtain the better performances and are also released in:**
	- CE-Net: [https://github.com/Guzaiwang/CE-Net](https://github.com/Guzaiwang/CE-Net) 
	- AG-Net: [https://github.com/HzFu/AGNet](https://github.com/HzFu/AGNet)
6. A pytorch implementation of M-Net could be found in **AG-Net**: [https://github.com/HzFu/AGNet](https://github.com/HzFu/AGNet)

---

**Main files:**

1. 'Step\_1\_Disc\_Crop.py': The disc detection code for whole funuds image.
2. 'Step\_2\_MNet\_train.py': The M-Net training code.
3. 'Step\_3\_MNet\_test.py': The M-Net testing code.
4. 'Step\_4\_CDR\_output.m': The ellipse fitting for disc and cup, and CDR calculation.

---

**If you use this code, please cite the following papers:**

1. Huazhu Fu, Jun Cheng, Yanwu Xu, Damon Wing Kee Wong, Jiang Liu, and Xiaochun Cao, "Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation", IEEE Transactions on Medical Imaging (TMI), vol. 37, no. 7, pp. 1597–1605, 2018. [[PDF]](https://arxiv.org/abs/1801.00926)  
2. Huazhu Fu, Jun Cheng, Yanwu Xu, Changqing Zhang, Damon Wing Kee Wong, Jiang Liu, and Xiaochun Cao, "Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image", IEEE Transactions on Medical Imaging (TMI), vol. 37, no. 11, pp. 2493–2501, 2018. [[PDF]](http://arxiv.org/abs/1805.07549)

**There are also some related works for medical image segmentation for your reference:**

1. "Attention Guided Network for Retinal Image Segmentation," in MICCAI, 2019. [[PDF]](http://arxiv.org/abs/1907.12930) [[Github Code]](https://github.com/Guzaiwang/CE-Net)
2. “CE-Net: Context Encoder Network for 2D Medical Image Segmentation,” IEEE TMI, 2019. [[PDF]](https://arxiv.org/abs/1903.02740) [[Github Code]](https://github.com/HzFu/AGNet)

---

**Note: for ORIGA and SCES datasets**

Unfortunately, the ORIGA and SCES datasets cannot be released due to the clinical policy.
But, here is an other glaucoma challenge, [**Retinal Fundus Glaucoma Challenge (REFUGE)**](https://refuge.grand-challenge.org/home/), including disc/cup segmentation, glaucoma screening, and localization of Fovea.

---
**License**

The code is released under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) for NonCommercial use only. Any commercial use should get formal permission first.

---

Update log:

- 19.01.22: Added training code, and uploaded the results on REFUGE dataset.
- 18.06.30: Added ellipse fitting code (based on Matlab), and Fixed the bug for macular center fundus.
- 18.06.29: Added disc detection code (based on U-Net).
- 18.02.26: Added CDR calculation code (based on Matlab).
- 18.02.24: Released the code.