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+# Automated Tissue Segmentation from High-Resolution 3D Steady-State MRI with Deep Learning
+
+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](results/3unet_vs_vnet_baseline/small_highwayless_train_result_2020_03_17-08_07_29.png "Small 3D Unet Highwayless Loss") | ![small-highway-less-progress](results/3unet_vs_vnet_baseline/small_highwayless_progress.gif "Small 3D Unet Highwayless Progress")
+
+
+<br />
+
+#### Small 3D Unet (160,160,160)
+
+Training Loss | Training Progress
+:------------:|:---------------------------:
+![small-3d-unet-loss](results/3unet_vs_vnet_baseline/small_3dunet_train_result_2020_03_17-09_34_10.png "Small 3D Unet Loss") | ![small-3d-unet-progress](results/3unet_vs_vnet_baseline/small_3dunet_progress.gif "Small 3D Unet Progress")
+
+
+<br />
+
+#### Small Relative 3D Unet (160,160,160),(3)
+
+Training Loss | Training Progress
+:------------:|:---------------------------:
+![small-relative-3d-unet-loss](results/3unet_vs_vnet_baseline/small_relative_3dunet_train_result_2020_03_17-11_03_20.png "Small Relative 3D Unet Loss") | ![small-relative-3d-unet-progress](results/3unet_vs_vnet_baseline/small_relative_3dunet_progress.gif "Small Relative 3D Unet Progress")
+
+
+<br />
+
+#### Small VNet (160,160,160)
+
+Training Loss | Training Progress
+:------------:|:---------------------------:
+![small-vnet-loss](results/3unet_vs_vnet_baseline/small_vnet_train_result_2020_03_17-12_37_32.png "Small VNet Loss") | ![small-vnet-progress](results/3unet_vs_vnet_baseline/small_vnet_progress.gif "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](results/3d_50_epoch_baseline/archer/train_session_2020_03_24-19_12_02_tiny/train_result_2020_03_24-19_12_02.png) | ![tiny_vnet_646464_progress](results/3d_50_epoch_baseline/archer/train_session_2020_03_24-19_12_02_tiny/progress.gif)
+
+#### Tiny VNet (160,160,160)
+
+Training Loss | Training Progress
+:------------:|:---------------------------:
+![tiny_vnet_160160160_loss](results/3d_50_epoch_baseline/archer/train_session_2020_03_24-20_07_26_tiny/train_result_2020_03_24-20_07_26.png) | ![tiny_vnet_160160160_progress](results/3d_50_epoch_baseline/archer/train_session_2020_03_24-20_07_26_tiny/progress.gif)
+
+#### Small VNet (160,160,160)
+
+Training Loss | Training Progress
+:------------:|:---------------------------:
+![small_vnet_160160160_loss](results/3d_50_epoch_baseline/archer/train_session_2020_03_25-05_09_08_small/train_result_2020_03_25-05_09_08.png) | ![small_vnet_160160160_progress](results/3d_50_epoch_baseline/archer/train_session_2020_03_25-05_09_08_small/progress.gif)
+
+#### Small Relative VNet (160,160,160),(3)
+
+Training Loss | Training Progress
+:------------:|:---------------------------:
+![small_rel_vnet_160160160_loss](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) | ![small_rel_vnet_160160160_progress](results/3d_50_epoch_baseline/archer/train_session_2020_03_25-06_57_15_small_relative/progress.gif)
+
+#### Small Slice VNet (160,160,5)
+
+Training Loss | Training Progress
+:------------:|:---------------------------:
+![small_slice_vnet_1601605_loss](results/3d_50_epoch_baseline/archer/train_session_2020_03_25-01_25_31_slice/train_result_2020_03_25-01_25_31.png) | ![small_slice_vnet_1601605_progress](results/3d_50_epoch_baseline/archer/train_session_2020_03_25-01_25_31_slice/progress.gif)
+
+#### Small VNet (240,240,160)
+
+Training Loss | Training Progress
+:------------:|:---------------------------:
+![small_vnet_240240160_loss](results/3d_50_epoch_baseline/pompeii/train_session_2020_03_25-06_39_15_small/train_result_2020_03_25-06_39_15.png) | ![small_vnet_240240160_progress](results/3d_50_epoch_baseline/pompeii/train_session_2020_03_25-06_39_15_small/progress.gif)
+
+#### Large VNet (240,240,160)
+
+Training Loss | Training Progress
+:------------:|:---------------------------:
+![large_vnet_240240160_loss](results/3d_50_epoch_baseline/pompeii/train_session_2020_03_24-13_06_17_large/train_result_2020_03_24-13_06_17.png) | ![large_vnet_240240160_progress](results/3d_50_epoch_baseline/pompeii/train_session_2020_03_24-13_06_17_large/progress.gif)
+
+#### Large Relative VNet (240,240,160),(3)
+
+Training Loss | Training Progress
+:------------:|:---------------------------:
+![large_rel_vnet_240240160_loss](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) | ![large_rel_vnet_240240160_progress](results/3d_50_epoch_baseline/pompeii/train_session_2020_03_25-04_10_24_large_relative/progress.gif)
+
+---
+
+### 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)
+3 | 64 | 32 | 16 | 2  | (3,3,3)
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