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Nov 28, 2022 •357 likes •550 views

GTC EUROPE 2017 Aidence Enhancing Radiology with Artificial Intelligence Localization in 3D Biomedical Image Data using Deep Learning Mark-Jan Harte, CEO GTC EUROPE 2017 About Aidence Founded in 2015, based in Amsterdam Deep learning

  1. GTC EUROPE 2017 Aidence Enhancing Radiology with Artificial Intelligence Localization in 3D Biomedical Image Data using Deep Learning Mark-Jan Harte, CEO

  2. GTC EUROPE 2017 About Aidence Founded in 2015, based in ● Amsterdam Deep learning for automatic ● medical image analysis 3 rd place in the Kaggle Data ● Science Bowl 2017 2

  3. GTC EUROPE 2017 Challenges for AI in Radiology Technical ○ Sample size Common resolution ImageNet: 256x256x3 = 196.608 Common resolution CT scan: 300x512x512x1 = 78.643.200 Class imbalances due to mostly healthy/background tissue present ○ ○ Accurate labeling is a pain Validation dataset for regulatory approval required ○ 3

  4. GTC EUROPE 2017 Lung Nodules CT Chest Early detection of lung nodules ● leads to 20% mortality reduction Human sensitivity ~80% ● Nodules are: Small ● Anywhere ● Highly variable in number ● 4

  5. GTC EUROPE 2017 Lung Nodules 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 5

  6. GTC EUROPE 2017 Lung Nodules 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 ○ 6

  7. GTC EUROPE 2017 Lumbar Foramina 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 ● 7

  8. GTC EUROPE 2017 Lumbar Foramina 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 ● 8

  9. GTC EUROPE 2017 Lumbar Foramina 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 9

  10. GTC EUROPE 2017 Lumbar Foramina 10

  11. GTC EUROPE 2017 Lumbar Foramina 11

  12. GTC EUROPE 2017 Lumbar Foramina Combating over-generalization Reduce the resolution for more context ● A segmentation per foramen level ● Bias towards the lower and bigger foramina ● Recall L1 L2 L3 L4 L5 Base 0 0 0 0.97 0.96 12

  13. GTC EUROPE 2017 Lumbar Foramina 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 ● 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 13

  14. GTC EUROPE 2017 Lumbar Foramina 14

  15. GTC EUROPE 2017 Lumbar Foramina 15

  16. GTC EUROPE 2017 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 16

  17. GTC EUROPE 2017 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 17

  18. GTC EUROPE 2017 “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 - markjan@aidence.com 18

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