Albert Ugwudike, Joe Arrowsmith, Joonsu Gha, Kamal Shah, Lapo Rastrelli, Olivia Gallupova, Pietro Vitiello

---

### 2D Models Implemented

- [x] SegNet 

- [x] Vanilla UNet 

- [x] Attention UNet

- [x] <del> Multi-res UNet </del>

- [x] R2_UNet

- [x] R2_Attention UNet

- [x] UNet++

- [x] 100-layer Tiramisu

- [x] DeepLabv3+ 

### 3D Models Implemented

- [x] 3D UNet

- [x] Relative 3D UNet

- [x] Slice 3D UNet

- [x] VNet

- [x] Relative VNet

- [x] Slice VNet

---

## Results

### Baseline Comparision of 3D Methods

| Model                     | Input Shape       | Loss  | Val Loss | Duration / Min  |

|---------------------------|-------------------|-------|----------|-----------------|

| Small Highwayless 3D UNet | (160,160,160)     | 0.777 | 0.847    | 86.6            |

| Small 3D UNet             | (160,160,160)     | 0.728 | 0.416    | 89.1            |

| Small Relative 3D UNet    | (160,160,160),(3) | 0.828 | 0.889    | 90.1            |

| Small VNet                | (160,160,160)     | 0.371 | 0.342    | 89.5            |

#### Small 3D Unet Highwayless (160,160,160)

Training Loss | Training Progress

:------------:|:---------------------------:

![small-highway-less-loss](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3unet_vs_vnet_baseline/small_highwayless_train_result_2020_03_17-08_07_29.png?raw=true"Small 3D Unet Highwayless Loss") | ![small-highway-less-progress](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3unet_vs_vnet_baseline/small_highwayless_progress.gif?raw=true "Small 3D Unet Highwayless Progress")

<br />

#### Small 3D Unet (160,160,160)

Training Loss | Training Progress

:------------:|:---------------------------:

![small-3d-unet-loss](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3unet_vs_vnet_baseline/small_3dunet_train_result_2020_03_17-09_34_10.png?raw=true "Small 3D Unet Loss") | ![https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/small-3d-unet-progress](results/3unet_vs_vnet_baseline/small_3dunet_progress.gif?raw=true "Small 3D Unet Progress")

<br />

#### Small Relative 3D Unet (160,160,160),(3)

Training Loss | Training Progress

:------------:|:---------------------------:

![small-relative-3d-unet-loss](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3unet_vs_vnet_baseline/small_relative_3dunet_train_result_2020_03_17-11_03_20.png?raw=true "Small Relative 3D Unet Loss") | ![small-relative-3d-unet-progress](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3unet_vs_vnet_baseline/small_relative_3dunet_progress.gif?raw=true "Small Relative 3D Unet Progress")

<br />

#### Small VNet (160,160,160)

Training Loss | Training Progress

:------------:|:---------------------------:

![small-vnet-loss](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3unet_vs_vnet_baseline/small_vnet_train_result_2020_03_17-12_37_32.png?raw=true "Small VNet Loss") | ![https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/small-vnet-progress](results/3unet_vs_vnet_baseline/small_vnet_progress.gif?raw=true "Small VNet Progress")

---

### Comparison of VNet Methods

|      Model     |      Input Shape  |        Loss   |      Val Loss  |     Roll Loss | Roll Val Loss |Duration / Min|

|:--------------:|:-----------------:|:-------------:|:--------------:|:-------------:|:-------------:|:------------:|

|      Tiny      |     (64,64,64)    | 0.627 ± 0.066 |  0.684 ± 0.078 | 0.652 ± 0.071 | 0.686 ± 0.077 |  61.5 ± 5.32 |

|      Tiny      |    (160,160,160)  | 0.773 ± 0.01  |  0.779 ± 0.019 | 0.778 ± 0.007 | 0.787 ± 0.016 | 101.8 ± 2.52 |

|      Small     |    (160,160,160)  | 0.648 ± 0.156 |  0.676 ± 0.106 | 0.656 ± 0.152 | 0.698 ± 0.076 | 110.1 ± 4.64 |

| Small Relative | (160,160,160),(3) | 0.653 ± 0.168 |  0.639 ± 0.176 | 0.659 ± 0.167 | 0.644 ± 0.172 | 104.6 ± 9.43 |

|      Slice     |     (160,160,5)   | 0.546 ± 0.019 |  0.845 ± 0.054 | 0.559 ± 0.020 | 0.860 ± 0.072 |  68.6 ± 9.68 |

|      Small     |    (240,240,160)  | 0.577 ± 0.153 |  0.657 ± 0.151 | 0.583 ± 0.151 | 0.666 ± 0.149 | 109.7 ± 0.37 |

|      Large     |    (240,240,160)  | 0.505 ± 0.262 |  0.554 ± 0.254 | 0.508 ± 0.262 | 0.574 ± 0.243 | 129.2 ± 0.50 |

| Large Relative | (240,240,160),(3) | 0.709 ± 0.103 |  0.880 ± 0.078 | 0.725 ± 0.094 | 0.913 ± 0.081 | 148.6 ± 0.20 |

```

Baseline results from training VNet models for 50 epochs, exploring how quick models converge. Models optimized for dice loss using a scheduled Adam optimizier. Start learning rate: $5e^{-5}$, Schedule drop: $0.9$, Schedule drop epoch frequency: $3$. Z-Score normalisation and replacement of outliers with mean pixel was applied to inputs. Subsamples were selected normally distributed from the centre. Github commit: cb39158

Optimal training session is choosen for each visulation.

```

#### Tiny VNet (64,64,64)

Training Loss | Training Progress

:------------:|:---------------------------:

![tiny_vnet_646464_loss](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/archer/train_session_2020_03_24-19_12_02_tiny/train_result_2020_03_24-19_12_02.png?raw=true) | ![tiny_vnet_646464_progress](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/archer/train_session_2020_03_24-19_12_02_tiny/progress.gif?raw=true)

#### Tiny VNet (160,160,160)

Training Loss | Training Progress

:------------:|:---------------------------:

![tiny_vnet_160160160_loss](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/archer/train_session_2020_03_24-20_07_26_tiny/train_result_2020_03_24-20_07_26.png?raw=true) | ![tiny_vnet_160160160_progress](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/archer/train_session_2020_03_24-20_07_26_tiny/progress.gif?raw=true)

#### Small VNet (160,160,160)

Training Loss | Training Progress

:------------:|:---------------------------:

![small_vnet_160160160_loss](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/archer/train_session_2020_03_25-05_09_08_small/train_result_2020_03_25-05_09_08.png?raw=true) | ![small_vnet_160160160_progress](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/archer/train_session_2020_03_25-05_09_08_small/progress.gif?raw=true)

#### Small Relative VNet (160,160,160),(3)

Training Loss | Training Progress

:------------:|:---------------------------:

![small_rel_vnet_160160160_loss](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/archer/train_session_2020_03_25-06_57_15_small_relative/train_result_2020_03_25-06_57_15.png?raw=true) | ![small_rel_vnet_160160160_progress](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/archer/train_session_2020_03_25-06_57_15_small_relative/progress.gif?raw=true)

#### Small Slice VNet (160,160,5)

Training Loss | Training Progress

:------------:|:---------------------------:

![small_slice_vnet_1601605_loss](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/archer/train_session_2020_03_25-01_25_31_slice/train_result_2020_03_25-01_25_31.png?raw=true) | ![small_slice_vnet_1601605_progress](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/archer/train_session_2020_03_25-01_25_31_slice/progress.gif?raw=true)

#### Small VNet (240,240,160)

Training Loss | Training Progress

:------------:|:---------------------------:

![small_vnet_240240160_loss](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/pompeii/train_session_2020_03_25-06_39_15_small/train_result_2020_03_25-06_39_15.png?raw=true) | ![small_vnet_240240160_progress](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/pompeii/train_session_2020_03_25-06_39_15_small/progress.gif?raw=true)

#### Large VNet (240,240,160)

Training Loss | Training Progress

:------------:|:---------------------------:

![large_vnet_240240160_loss](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/pompeii/train_session_2020_03_24-13_06_17_large/train_result_2020_03_24-13_06_17.png?raw=true) | ![large_vnet_240240160_progress](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/pompeii/train_session_2020_03_24-13_06_17_large/progress.gif?raw=true)

#### Large Relative VNet (240,240,160),(3)

Training Loss | Training Progress

:------------:|:---------------------------:

![large_rel_vnet_240240160_loss](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/pompeii/train_session_2020_03_25-04_10_24_large_relative/train_result_2020_03_25-04_10_24.png?raw=true) | ![large_rel_vnet_240240160_progress](https://github.com/3D-MRI-Tissue-Segmentation/knee-segmentation/blob/master/results/3d_50_epoch_baseline/pompeii/train_session_2020_03_25-04_10_24_large_relative/progress.gif?raw=true)

---

### Useful Code Snippets

``` Bash

Run 3D Train

python Segmentation/model/vnet_train.py

```

``` Bash

Unit-Testing and Unit-Test Converage

python -m pytest --cov-report term-missing:skip-covered --cov=Segmentation && coverage html && open ./htmlcov.index.html

```

``` Bash

Start tensorboard on Pompeii

On pompeii: tensorboard --logdir logs --samples_per_plugin images=100

On your local machine: ssh -L 16006:127.0.0.1:6006 username@ip

Go to localhost: http://localhost:16006/

```

---

### Valid 3D Configs

Batch / GPU | Crop Size | Depth Crop Size | Num Channels | Num Conv Layers | Kernel Size

:----------:|:--------:|:---------------:|:------------:|:---------------:|:----------:

1 | 32 | 32 | 20 | 2  | (5,5,5)

1 | 64 | 64 | 32 | 2  | (3,3,3)

1 | 64 | 64 | 32 | 2  | (5,5,5)