Post on 12-Mar-2021
GTC EUROPE 2017
AidenceEnhancing Radiology with Artificial Intelligence
Localization in 3D Biomedical Image Datausing Deep Learning
Mark-Jan Harte, CEO
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About Aidence
● Founded in 2015, based in Amsterdam
● Deep learning for automatic medical image analysis
● 3rd place in the Kaggle Data Science Bowl 2017
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Challenges for AI in Radiology
Technical
○ Sample size
○ Class imbalances due to mostly healthy/background tissue present
○ Accurate labeling is a pain
○ Validation dataset for regulatory approval required
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Common resolution ImageNet: 256x256x3 = 196.608
Common resolution CT scan: 300x512x512x1 = 78.643.200
GTC EUROPE 2017
CT Chest
● Early detection of lung nodules leads to 20% mortality reduction
● Human sensitivity ~80%
Nodules are:
● Small● Anywhere● Highly variable in number
Lung Nodules
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Detection
Training
● Input is 128x128x7 voxels● Target is 58x58 rectangle mask● Loss: Normalized cross entropy● Output: segmentation (probability map)● Keep fine grained spatial details● Network size not too big● Second network to filter out false positives
○ Larger, less restrictions
Lung Nodules
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Network architecture
● Fully 3D convolutional○ Efficient inference on big CT scans○ No same padding○ No pooling○ No strides
● Dilated convolutions ○ Reduce resolution more quickly○ Keep network size in check (180K params)
● Weight normalization [Salimans & Kingma, 2016]
○ Easier (than batch norm) to distribute over multiple GPUs
Lung Nodules
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MR Lumbar Spine
● Foramen is the passage where a nerve exits the spine
● Foraminal stenosis is a common cause of leg pain
○ Time-consuming to find on scoliotic Spines
● Task: locate and classify all of them
Lumbar Foramina
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Localization
● Foramina are big● Foramina are located at a certain
location● There are 10 lumbar foramina per spine
● Output a segmentation (probability map)● Requires less fine grained details
Lumbar Foramina
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Localization architecture
● 3D U-Net architecture○ Easier to modify and/or extend○ Less need for efficient inference○ Input: 481x481x3
● Binary segmentation (probability map)
● Loss: (Fuzzy) Dice score○ Trains faster and requires less data for our
network than the normalized cross entropy loss
Lumbar Foramina
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Lumbar Foramina
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Lumbar Foramina
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Combating over-generalization
● Reduce the resolution for more context● A segmentation per foramen level
● Bias towards the lower and bigger foramina
Lumbar Foramina
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Recall L1 L2 L3 L4 L5
Base 0 0 0 0.97 0.96
GTC EUROPE 2017
Combating over-generalization
● Reduce the resolution for more context● A segmentation per foramen level
● Bias towards the lower and bigger foramina
● Normalize the Dice score
Lumbar Foramina
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Recall L1 L2 L3 L4 L5
Base 0 0 0 0.97 0.96
Normalized 0.87 0.96 0.97 0.98 0.95
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Lumbar Foramina
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Lumbar Foramina
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A Comparison
● Lung nodules:○ Challenge:
■ Detect small nodules in a vast volume■ Requires fine grained spatial details
○ Solution:■ Fully convolutional for efficient inference■ Dilated convolutions to keep network size in check■ Trains on 10,000s of samples
● Lumbar foramina ○ Challenge:
■ Detect big foramina in a (relatively) small volume■ Distinguish lumbar foramina from thoracic ones
○ Solution:■ Use 3D U-Net with Dice score■ Normalize the Dice score and reduce the resolution for increased performance■ Trains on 100s of samples
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In practice
● FDA and CE clearance necessary for diagnostic impact○ Certify the training pipeline, inference pipeline, annotation tooling, deployment…○ Concept of independent test set matches very well○ Discussions on continuous learning (FDA, ACR)
● Aidence lung nodule detection submitted for CE○ Feedback received; clearance expected soon
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“Your software has, in this short time, detected a patient with a nodule that has clearly grown in 3 years, is probably malignant and missed by 4 consecutive radiologists.”
- W.M, radiologist
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markjan@aidence.com