The purpose of this repository is to maintain a version of state-of-the-art segmentation method for general medical image segmentation tasks.
Current version: Resnet101 based atrous convolutional nerual network. (a modified version of DeeplabV3) April, 2020
This is a PyTorch implementation of the 2D state-of-the-art method backbone of DeepLabV3, parameters and changes are done to fit CT/MRI data and medical image segmentation. The original paper:DeepLabv3 that aims to reuse the resnet implementation in torchvision as much as possible. This means we use the PyTorch model checkpoint
We try to match every detail in original network, except to fit for medical segmentation tasks.
(GPU with at least 11GB memory is required). Also support the combination of Group Normalization + Weight Standardization:
| Implementation | Normalization | Multi-Grid | ASPP | Image Pooling | mIOU |
|---|---|---|---|---|---|
| Paper | BN | (1, 2, 4) | (6, 12, 18) | Yes | 77.21 |
| Ours | BN | (1, 1, 1) | (6, 12, 18) | Yes | 76.49 |
| Ours | GN+WS | (1, 1, 1) | (6, 12, 18) | Yes | 77.20 |
To run the BN experiment, after preparing the dataset as follows, simply run:
python main.py --train --exp bn_lr7e-3 --epochs 50 --base_lr 0.007mkdir data
cd data |
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 |
|---|
mkdir data
cd data
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
tar -xf VOCtrainval_11-May-2012.tar
cd VOCdevkit/VOC2012/
wget http://cs.jhu.edu/~cxliu/data/SegmentationClassAug.zip
wget http://cs.jhu.edu/~cxliu/data/SegmentationClassAug_Visualization.zip
wget http://cs.jhu.edu/~cxliu/data/list.zip
unzip SegmentationClassAug.zip
unzip SegmentationClassAug_Visualization.zip
unzip list.zipunzip leftImg8bit_trainvaltest.zip
unzip gtFine_trainvaltest.zip
git clone https://github.com/mcordts/cityscapesScripts.git
mv cityscapesScripts/cityscapesscripts ./
rm -rf cityscapesScripts
python cityscapesscripts/preparation/createTrainIdLabelImgs.py