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segmentation_models_3D

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Set of models for segmentation of 3D volumes

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# 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}
}
```

segmentation_models_3D

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