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https://github.com/zfturbo/segmentation_models_3d
Set of models for segmentation of 3D volumes
https://github.com/zfturbo/segmentation_models_3d
3d-segmentation 3d-semantic-segmentation keras segmentation
Last synced: 4 days ago
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Set of models for segmentation of 3D volumes
- Host: GitHub
- URL: https://github.com/zfturbo/segmentation_models_3d
- Owner: ZFTurbo
- License: mit
- Created: 2020-07-31T15:50:59.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-06-29T10:52:35.000Z (7 months ago)
- Last Synced: 2024-10-15T20:17:31.693Z (3 months ago)
- Topics: 3d-segmentation, 3d-semantic-segmentation, keras, segmentation
- Language: Python
- Homepage:
- Size: 66.4 KB
- Stars: 122
- Watchers: 4
- Forks: 27
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- License: LICENSE
Awesome Lists containing this project
README
# Segmentation models 3D Zoo for Keras 3
The repository contains 3D variants of popular models for segmentation like FPN, Unet, Linknet and PSPNet.
This repository is based on great [segmentation_models](https://github.com/qubvel/segmentation_models) repo by [@qubvel](https://github.com/qubvel/)
### Available architectures:
- [Unet](https://arxiv.org/abs/1505.04597)
- [FPN](http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf)
- [Linknet](https://arxiv.org/abs/1707.03718)
- [PSPNet](https://arxiv.org/abs/1612.01105)
### Installation
`pip install segmentation-models-3D`
### Examples
##### Loading model:
```python
import segmentation_models_3D as sm
model1 = sm.Unet(
'resnet34',
encoder_weights='imagenet'
)
# binary segmentation (these parameters are default when you call Unet('resnet34')
model2 = sm.FPN(
'densenet121',
classes=1,
activation='sigmoid'
)
# multiclass segmentation with non overlapping class masks (your classes + background)
model3 = sm.Linknet(
'resnet34',
classes=3,
activation='softmax'
)
# multiclass segmentation with independent overlapping/non-overlapping class masks
model4 = sm.PSPNet(
'resnet34',
classes=3,
activation='sigmoid'
)
# If you need to specify non-standard input shape
model5 = sm.Unet(
'resnet50',
input_shape=(96, 128, 128, 6),
encoder_weights=None
)
```
All possible backbones: `
'resnet18, 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'seresnet18',
'seresnet34', 'seresnet50', 'seresnet101', 'seresnet152', 'seresnext50',
'seresnext101', 'senet154', 'resnext50', 'resnext101', 'vgg16', 'vgg19',
'densenet121', 'densenet169', 'densenet201', 'inceptionresnetv2',
'inceptionv3', 'mobilenet', 'mobilenetv2', 'efficientnetb0',
'efficientnetb1', 'efficientnetb2', 'efficientnetb3', 'efficientnetb4',
'efficientnetb5', 'efficientnetb6', 'efficientnetb7', 'efficientnetv2-b1',
'efficientnetv2-b2', 'efficientnetv2-b3', 'efficientnetv2-s',
'efficientnetv2-m', 'efficientnetv2-l'
`
More examples can be found in:
- Tensorflow: [tst_keras_tensorflow.py](tst_keras_tensorflow.py)
- Torch: [tst_keras_torch.py](tst_keras_torch.py)
- Jax: [tst_keras_jax.py](tst_keras_jax.py)
##### Training model:
There is training example in [training_example_tensorflow.py](training_example_tensorflow.py)
* I tried to keep code as simple as possible
* I couldn't find good dataset for 3D segmentation task. So I randomly generate 3D volumes with dark background with light
figures (spheres and cuboids) and model tries to segment these figures independetly. 1st mask for circles and 2nd mask for cuboids.
### To Do List
* Add `stride_size` parameter for better control of models
### Related repositories
* [https://github.com/qubvel/classification_models](https://github.com/qubvel/classification_models) - original classification 2D repo
* [https://github.com/qubvel/segmentation_models](https://github.com/qubvel/segmentation_models) - original segmentation 2D repo
* [classification_models_3D](https://github.com/ZFTurbo/classification_models_3D) - models for classification in 3D
* [segmentation_models_pytorch_3d](https://github.com/ZFTurbo/segmentation_models_pytorch_3d) - models for segmentation in 3D for Pytorch
* [volumentations](https://github.com/ZFTurbo/volumentations) - 3D augmentations
### Unresolved problems
* There is no 'bilinear' interpolation for UpSample3D layer, so it uses Nearest Neighbour upsampling.
### Older versions
Last version which supports Keras 2 is 1.0.7
`pip install segmentation-models-3D==1.0.7`
## Citation
For more details, please refer to the publication: https://doi.org/10.1016/j.compbiomed.2021.105089
If you find this code useful, please cite it as:
```
@article{solovyev20223d,
title={3D convolutional neural networks for stalled brain capillary detection},
author={Solovyev, Roman and Kalinin, Alexandr A and Gabruseva, Tatiana},
journal={Computers in Biology and Medicine},
volume={141},
pages={105089},
year={2022},
publisher={Elsevier},
doi={10.1016/j.compbiomed.2021.105089}
}
```