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BraTS2018_NvNet

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Implementation of NvNet

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# BraTS_NvNet

This is an **unofficial** *Pytorch* implementation of BraTS_NvNet, based on **Andriy Myronenko's** paper on [**MICCAI_BraTS_2018_proceedings_shortPapers**](https://www.cbica.upenn.edu/sbia/Spyridon.Bakas/MICCAI_BraTS/MICCAI_BraTS_2018_proceedings_shortPapers.pdf)

## Overview

BraTS2018: [Multimodal Brain Tumor Segmentation Challenge 2018](https://www.med.upenn.edu/sbia/brats2018.html)

The participants are called to address this task by using the provided clinically-acquired training data to develop their method and produce segmentation labels of the different glioma sub-regions. **The sub-regions considered for evaluation are: 1) the "enhancing tumor" (ET), 2) the "tumor core" (TC), and 3) the "whole tumor" (WT)**.

Segmentation Labels:
  - WT: 2
  - TC: 1 
  - ET: 4
## Network

A combination of Vnet and VAE(variation auto-encoder).

**Table 1.** Encoder structure, where GN stands for group normalization(with group size of 8), Conv - 3\*3\*3 convolution, AddId - addition of identity / skip connection. Repeat column shows the number of repetitions of the block.

| Name | Ops | Repeat | Output size|
  | :---- | :----------------: | :--: | :--------: |
  | Input | | | 4\*160\*192\*128 |    
  | InitConv | Conv | 1 | 32\*160\*192\*128 |
  | EncoderBlock0 | GN, ReLU, Conv, GN, ReLU, Conv, AddId | 1 | 32\*160\*192\*128 |
  | EncoderDown1 | Conv stride 2 | 1 | 64\*80\*96\*64 |
  | EncoderBlock1 | GN, ReLU, Conv, GN, ReLU, Conv, AddId | 2 | 64\*80\*96\*64 |
  | EncoderDown2 | Conv stride 2 | 1 | 128\*40\*48\*32 |
  | EncoderBlock2 | GN, ReLU, Conv, GN, ReLU, Conv, AddId | 2 | 128\*40\*48\*32 |
  | EncoderDown3 | Conv stride 2 | 1 | 256\*20\*24\*16 |
  | EncoderBlock3 | GN, ReLU, Conv, GN, ReLU, Conv, AddId | 4 | 256\*20\*24\*16 |

**Table 2.** Decoder structure, where Conv1 stands for 1\*1\*1 convolution, UpLinear - 3D linear spatial upsampling.

| Name | Ops | Repeat | Output size|
  | :---- | :----------------: | :--: | :--------: |
  | DecoderUp2 | Conv1, UpLinear, +EncoderBlock2 | 1 | 128\*40\*48\*32 |    
  | DecoderBlock2 | GN, ReLU, Conv, GN, ReLU, Conv, AddId | 1 | 128\*40\*48\*32 |
  | DecoderUp1 | Conv1, UpLinear, +EncoderBlock1 | 1 | 64\*80\*96\*64 |    
  | DecoderBlock1 | GN, ReLU, Conv, GN, ReLU, Conv, AddId | 1 | 64\*80\*96\*64 |
  | DecoderUp0 | Conv1, UpLinear, +EncoderBlock0 | 1 | 32\*160\*192\*128 |    
  | DecoderBlock0 | GN, ReLU, Conv, GN, ReLU, Conv, AddId | 1 | 32\*160\*192\*128 |
  | DecoderEnd | Conv1, Sigmoid | 1 | 1\*160\*192\*128 |

**Table 3.** VAE decoder branch structure, where Dense stands for fully connected layer.

| Name | Ops | Repeat | Output size|
  | :---- | :----------------: | :--: | :--------: |
  | VD | GN, ReLU, Conv (16) stride 2, Dense (256) | 1 | 256\*1|
  | VDraw | sample | 1 | 128\*1|
  | VU | Dense, ReLU, Conv1, UpLinear | 1 | 256\*20\*24\*16 |
  | VUp2 | Conv1, UpLinear | 1 | 128\*40\*48\*32 |    
  | VBlock2 | GN, ReLU, Conv, GN, ReLU, Conv, AddId | 1 | 128\*40\*48\*32 |
  | VUp1 | Conv1, UpLinear | 1 | 64\*80\*96\*64 |    
  | VBlock1 | GN, ReLU, Conv, GN, ReLU, Conv, AddId | 1 | 64\*80\*96\*64 |
  | VUp0 | Conv1, UpLinear | 1 | 32\*160\*192\*128 |    
  | VBlock0 | GN, ReLU, Conv, GN, ReLU, Conv, AddId | 1 | 32\*160\*192\*128 |
  | Vend | Conv1 | 1 | 4\*160\*192\*128 |

sample ~ ![sample](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/sample_N.gif)

## Loss

1. The loss function consists of 3 terms:

![loss](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/combined_Loss.gif)
  
  
2. ![L_dice](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/L_dice.gif) is applied to the decoder output ![p_pred](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/p_pred.gif) to match the segmentation mask ![p_true](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/p_true.gif) :

![soft_dice_loss](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/soft_dice_Loss.gif)
  
    where summation is voxel-wise, and the ![epsilon](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/epsilon.gif) is a small constant to avoid zero division.
  
3. ![L2](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/L2.gif) is appiled on the VAE branch output ![I_pred](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/I_pred.gif) to match the input image ![I_input](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/I_input.gif) :

![l2_loss](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/L2_Loss.gif)
  
4. ![L_KL](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/L_KL.gif) is standard VAE penalty term, a KL divergence between the estimated normal distribution ![est_norm](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/est_norm.gif) and a prior distribution ![pri_norm](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/pri_norm.gif), which has a closed form representation:

![KL_Loss](https://raw.githubusercontent.com/athon2/BraTS2018_NvNet/master/doc/KL_Loss.gif)
  
    where N is total number of image voxels.
 
5. The hyper-parameter weight of 0.1 was empirically set to provide a good balance between dice and VAE loss terms.

## Dependencies

```
python: 3.6.2
pytorch: 0.4.1
pytables: 3.4.4
numpy: 1.13.1
tensorboardX: 1.4
nibabel: 2.3.0
nilearn: 0.4.2
tqdm: 4.26.0
pickle: 0.7.4

```
## Usage

1. data preprocessing: 
  - change the directory of brats 2018 training dataset and validation dataset
  - set dataset format
  - run the script：
  ```
    python3 data_preprocess.py
  ```
2. train model: 
 - set training data file path
 - set training parameters
 - run the script:
  ```
    python3 main.py
  ```
3. predict:
  - set validation data file path
  - set model file path
  - run the script:
  ```
    python3 predict.py
  ```
4. make submission:
  - set prediction file path
  - set reconstruction parameters
  - run the script:
  ```
    python3 make_submission.py
  ```

## Implementation Details

- [x] NvNet 
- [x] Loss
- [x] DataLoader
- [x] Train
- [x] Predict
- [x] Data Preprocess
- [x] Make Submission

BraTS2018_NvNet

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