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

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2D Models Implemented

• SegNet
• Vanilla UNet
• Attention UNet
• Multi-res UNet
• R2_UNet
• R2_Attention UNet
• UNet++
• 100-layer Tiramisu
• DeepLabv3+

3D Models Implemented

• 3D UNet
• Relative 3D UNet
• Slice 3D UNet
• VNet
• Relative VNet
• Slice VNet

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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

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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

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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/

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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)