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BRAIN TUMOR SEGMENTATION
Lars Sjösund
Senior AI Research Engineer
Peltarion
Vice Chairman - Stockholm AI
Jens Sjölund
PhD, Medical Imaging
Linköping University / Elekta
1.
PROBLEM
BACKGROUND
2.
U-NET
3.
TIRAMISU
4.
HANDS ON
DEMO
14 million new cancer cases per year
2/5 diagnosed at some point in life
Sources: http://www.who.int/mediacentre/factsheets/fs297/en/
https://seer.cancer.gov/statfacts/html/all.html
BRAIN TUMOR
SEGMENTATION
PROBLEM SETUP
276 patients - 3D MRI scans
4 input channels - 4 tumor classes
INPUT TARGET
Flair T1 T1c T2 Tumor map
Source: http://braintumorsegmentation.org/
Can be fed raw data, adapt, learn to interpret and conclude
ARTIFICIAL NEURAL NETWORKS
AUTOENCODER
INPUT OUTPUTENCODER DECODER
AUTOENCODER
INPUT OUTPUTENCODER DECODER
U-NET
INPUT OUTPUT
INPUT OUTPUT
3x3
Conv Dropout ReLU Pooling
INPUT OUTPUT
3x3
Conv Dropout ReLU Upsampling
Initial Results
97,6% pixel level accuracy!
TARGET PREDICTEDINPUT
Initial Results
2.4%
97.6%
Class Balance
No tumor Tumor
L = − pij log !pij
j
∑
i
∑
ˆL = −
1
fj
pij log !pij
j
∑
i
∑
2.4%
97.6%
Class Balance
No tumor Tumor
Weighted Loss Results
TARGET PREDICTEDINPUT
Mistakes
TARGET PREDICTEDINPUT
Results - Similarity scores
Doctors U-Net with weighted loss
Whole 85% (±8%) 82% (±16%)
Core 75% (±24%) 71% (±21%)
Enhancing 74% (±13%) 60% (±30%)
DenseNet / Tiramisu
Source: https://arxiv.org/abs/1608.06993
DenseNet / Tiramisu
Sources: https://arxiv.org/abs/1608.06993
https://arxiv.org/abs/1611.09326
Results - Similarity scores
Doctors
U-Net with
weighted loss
Tiramisu with
weighted loss
Tiramisu w/o
weighted loss
Whole 85% (±8%) 82% (±16%) 81%(±17%) 84%(±14%)
Core 75% (±24%) 71% (±21%) 69%(±22%) 66%(±24%)
Enhancing 74% (±13%) 60% (±30%) 60%(±31%) 61%(±31%)
#CONFIGS
TRIED

~ 100
< 1000 

LINES OF CODE



~20 FOR MODEL
TIME PER
CONFIG 

< 13 HOURS
TENSORFLOW,
KERAS,
TENSORBOARD
GPU
4 TITAN X
12 GB
256 GB
RAM
40 CORES
OF INTEL
XEON
E5-2630
COST

100K SEK
Models trained on in-house desktop
Demo time!
Thank you!
Lars Sjösund
lars@peltarion.com
@sjosund
Questions?
M A K I N G A I A V A I L A B L E F O R E V E R Y O N E

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