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3D-MRI-Tissue-Segmentation / knee-segmentation

Automatic Segmentation of Knee Cartilage from MRI using Deep Learning
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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

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-highway-less-loss small-highway-less-progress


Small 3D Unet (160,160,160)

Training Loss Training Progress
small-3d-unet-loss small-3d-unet-progress


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

Training Loss Training Progress
small-relative-3d-unet-loss small-relative-3d-unet-progress


Small VNet (160,160,160)

Training Loss Training Progress
small-vnet-loss 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 tiny_vnet_646464_progress

Tiny VNet (160,160,160)

Training Loss Training Progress
tiny_vnet_160160160_loss tiny_vnet_160160160_progress

Small VNet (160,160,160)

Training Loss Training Progress
small_vnet_160160160_loss small_vnet_160160160_progress

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

Training Loss Training Progress
small_rel_vnet_160160160_loss small_rel_vnet_160160160_progress

Small Slice VNet (160,160,5)

Training Loss Training Progress
small_slice_vnet_1601605_loss small_slice_vnet_1601605_progress

Small VNet (240,240,160)

Training Loss Training Progress
small_vnet_240240160_loss small_vnet_240240160_progress

Large VNet (240,240,160)

Training Loss Training Progress
large_vnet_240240160_loss large_vnet_240240160_progress

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

Training Loss Training Progress
large_rel_vnet_240240160_loss large_rel_vnet_240240160_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)