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
3D Models Implemented
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 3D Unet (160,160,160)
| Training Loss |
Training Progress |
 |
 |
Small Relative 3D Unet (160,160,160),(3)
| Training Loss |
Training Progress |
 |
 |
Small VNet (160,160,160)
| Training Loss |
Training 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 (160,160,160)
| Training Loss |
Training Progress |
 |
 |
Small VNet (160,160,160)
| Training Loss |
Training Progress |
 |
 |
Small Relative VNet (160,160,160),(3)
| Training Loss |
Training Progress |
 |
 |
Small Slice VNet (160,160,5)
| Training Loss |
Training Progress |
 |
 |
Small VNet (240,240,160)
| Training Loss |
Training Progress |
 |
 |
Large VNet (240,240,160)
| Training Loss |
Training Progress |
 |
 |
Large Relative VNet (240,240,160),(3)
| Training Loss |
Training Progress |
 |
 |
Useful Code Snippets
Run 3D Train
python Segmentation/model/vnet_train.py
Unit-Testing and Unit-Test Converage
python -m pytest --cov-report term-missing:skip-covered --cov=Segmentation && coverage html && open ./htmlcov.index.html
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) |
