Author: Yuchao Gu
E-mail: 2015014178@buct.edu.cn
Date: 2018-05-27
Description: The code is an pytorch implementation of 《Iterative Deep Learning for Network Topology Extraction》. But I have not finished it, it is still in the patch training step.
DRIVE: Digital Retinal Images for Vessel Extraction You can download raw data here. Then we need to do some processes to generate our train data:
1、Randomly choose a foreground pixel as the center pixel, then crop a patch(64*64) around the center pixel.
2、Find pixels at the boder which are foreground. In order to detect whether they are connected to the center pixel, we make a shortest path from the border to the center. If cost of this path is smaller than INF(we set background INF), we generate a Gaussian peak around this pixel.
left is patch data, middle is label and right is our connective model ouput
python patchConnectionTrain.py
tensorboard --logdir=log
This code depends on the following libraries:
- Python 3.6
- Pytorch
- TensorboardX
- Scikit-image
- PIL
hourglass-network
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├── retinapatch.zip # my preprocessed train data and label
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├── gyclab # my utils for image segmentation
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├── log # tensorboard visiualize
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├── readmeDisplay # some images for readme
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├── result # train output
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├── genConnectiveData.py # my implementation to generate data for patch model
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├── patchConnectionTrain.py # train code
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└── readme.md # introduce to this project