HIVE-Net
所属分类：云数据库/云存储
开发工具：Python
文件大小：0KB
下载次数：0
上传日期：2020-05-12 14:30:33
上 传 者：
sh-1993
说明： HIVE-Net：用于EM图像线粒体分割的中心线感知分层视图集成卷积网络，
(HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images,)
文件列表:
.DS_Store (6148, 2020-05-12)
accuracy.py (2793, 2020-05-12)
advanced_model.py (11905, 2020-05-12)
aug_pre.py (2786, 2020-05-12)
cal_crop_num.py (574, 2020-05-12)
cal_dice_jac_3d.py (2853, 2020-05-12)
change_img2nii.py (826, 2020-05-12)
configFile/ (0, 2020-05-12)
configFile/.DS_Store (6148, 2020-05-12)
configFile/__pycache__/ (0, 2020-05-12)
configFile/__pycache__/add_argument.cpython-36.pyc (4753, 2020-05-12)
configFile/add_argument.py (6966, 2020-05-12)
createVal.py (4238, 2020-05-12)
dataset.py (17397, 2020-05-12)
figures/ (0, 2020-05-12)
figures/network.png (635571, 2020-05-12)
losses.py (3838, 2020-05-12)
main.py (7070, 2020-05-12)
mean_std.py (1753, 2020-05-12)
metrics.py (5609, 2020-05-12)
mito_prediction.py (7336, 2020-05-12)
modules.py (9976, 2020-05-12)
post_processing.py (3406, 2020-05-12)
pre_processing.py (11511, 2020-05-12)
save_history.py (1249, 2020-05-12)
showImg.py (914, 2020-05-12)
util/ (0, 2020-05-12)
util/__pycache__/ (0, 2020-05-12)
util/__pycache__/data_aug.cpython-35.pyc (1523, 2020-05-12)
util/__pycache__/data_aug.cpython-36.pyc (1135, 2020-05-12)
util/__pycache__/get_slice.cpython-36.pyc (3849, 2020-05-12)
util/__pycache__/parse_config.cpython-36.pyc (2426, 2020-05-12)
util/__pycache__/tools_self.cpython-36.pyc (2154, 2020-05-12)
util/data_aug.py (2050, 2020-05-12)
util/get_slice.py (4006, 2020-05-12)
util/parse_config.py (3217, 2020-05-12)
util/tools_self.py (2238, 2020-05-12)
# HIVE-Net-Centerline-Aware-HIerarchical-View-Ensemble-Convolutional-Network-for-Mitochondria-Segment
Here are implementations for paper:
**HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images**.
Contact: Zhimin Yuan (zhimin_yuan@163.com)
## Network Structure
## Requirements
- Tested with Python 3.6
- CUDA 9.0 or higher
- PyTorch 1.1.0
- numpy 1.16.4
- albumentations 0.3.0
## Task
Mitochondria segmentation
## Dataset
1. EPFL dataset: https://www.epfl.ch/labs/cvlab/data/data-em/
2. Kasthuri++ dataset: https://sites.google.com/view/connectomics/home
The data augmentation library [Albumentation](https://github.com/dream-toy/HIVE-Net/blob/master/https://github.com/albumentations-team/albumentations)
## Training
Run `main.py` either in your favorite Python IDE or the terminal by typing:
```
python main.py
```
## Testing
Run `mito_prediction.py` either in your favorite Python IDE or the terminal by typing:
```
python mito_prediction.py
```
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