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DIS17 Ruben Hemelings - Deep Learning on Biomedical Images
- 1. Ruben Hemelings Graduate Researcher @ VITO – KU Leuven Data Innova)on Summit March, 30 2017 #DIS2017 Deep learning on biomedical images
- 2. Ruben Hemelings30th of March Research – Automated analysis of blood vessels with deep learning
- 3. Topics to cover Context – Medical and technological motivation Deep learning applied 1 2 Session outline
- 4. Intro A quick refresher on the anatomy of the human eye 1 I think we all know this…
- 5. Intro A quick refresher on the anatomy of the human eye 1 But do you remember this?
- 6. Context Research proposal – Quick overview Quick overview 1 1 2 3 1 Fundus photography 2 Vessel extraction 3 Artery/Vein labelling Automated analysis of blood vessels with deep learning Focus area
- 7. 7 Analysis of the retina offers a non-intrusive way of diagnosing ocular aberrations, but also systemic diseases 7 Medical motivationContext 2 Ocular diseases 1 Systemic diseases 2 Image of the human retina Fovea Optic disc • Diabetic retinopathy • Glaucoma • Ocular hypertension • Cardiovascular disease (CVD) • Alzheimer • Atherosclerosis Rods and cones - zoom
- 8. 8 Analysis of the retina offers a non-intrusive way of diagnosing ocular aberrations, but also systemic diseases 8 Medical motivationContext 3 Ocular diseases 1 Systemic diseases 2 Image of the human retina Fovea Optic disc • Diabetic retinopathy • Glaucoma • Ocular hypertension • Cardiovascular disease (CVD) • Alzheimer • Atherosclerosis Hard exudate Hemorrhage
- 9. 9 Analysis of the retina offers a non-intrusive way of diagnosing ocular aberrations, but also systemic diseases 9 Medical motivationContext 4 Ocular diseases 1 Systemic diseases 2 Image of the human retina Fovea Cupping of optic disc • Diabetic retinopathy • Glaucoma • Ocular hypertension • Cardiovascular disease (CVD) • Alzheimer • Atherosclerosis Narrowing arterioles
- 10. 10 Analysis of the retina offers a non-intrusive way of diagnosing ocular aberrations, but also systemic diseases 10 Medical motivationContext 5 Ocular diseases 1 Systemic diseases 2 Image of the human retina Fovea Optic disc • Diabetic retinopathy • Glaucoma • Ocular hypertension • Cardiovascular disease (CVD) • Alzheimer • Atherosclerosis Narrowing arterioles Narrowing venules
- 11. 11 Analysis of the retina offers a non-intrusive way of diagnosing ocular aberrations, but also systemic diseases 11 Medical motivationContext 6 Ocular diseases 1 Systemic diseases 2 Image of the human retina Fovea Optic disc • Diabetic retinopathy • Glaucoma • Ocular hypertension • Cardiovascular disease (CVD) • Alzheimer • Atherosclerosis Narrowing arterioles Widening venules (Hypertension, atherosclerosis) Narrowing venules (Alzheimer) SIMILAR SYMPTOMS OBSERVABLE IN VASCULATURE
- 12. 12 Analysis of the retina offers a non-intrusive way of diagnosing ocular aberrations, but also systemic diseases 12 Medical motivationContext 7 Ocular diseases 1 Systemic diseases 2 Fovea Optic disc LINKED CRAE = Average artery width CRVE = Average vein widthA/V Ratio How to compute A/V ratio in automated setting? = Image segmentation problem
- 13. 13 Image segmentation methods based on deep learning are currently outperforming other methods… 13 Technological motivationContext 8 PIXEL-WISE CLASSIFICATION COMPETITION TO LABEL 20 TYPES OF OBJECTS, DOMINATED BY DEEP LEARNING ARCHITECTURES
- 14. 14 But has not been applied on the A/V classification of retinal blood vessels up to now 14 Technological motivationContext 9 1 2 3 PIXEL-WISE CLASSIFICATION ALREADY APPLIED NOT APPLIED THUSFAR…
- 15. 15 Deep learning is a set of machine learning architectures inspired by the structure and function of the human brain 15 Deep learning explainedDeep learning applied 13 X W + b Y SIMPLE NEURAL NETWORK Size Price Y = w * X +b IS THIS THE BEST LINE? One perceptron
- 16. 16 Deep learning is a set of machine learning architectures inspired by the structure and function of the human brain 16 Deep learning explainedDeep learning applied 13 X W + b Y SIMPLE NEURAL NETWORK Size Price Y = w * X +b OPTIMIZE LOSS FUNCTION Here: Mean Square Error (MSE) One perceptron
- 17. 17 Deep learning is a set of machine learning architectures inspired by the structure and function of the human brain 17 Deep learning explainedDeep learning applied 13 X W + b Y IMAGE SIMPLE NEURAL NETWORK Size Price Y = w * X +b OPTIMIZE LOSS FUNCTION Here: Mean Square Error (MSE) HOW DOES THIS APPLY ON IMAGES? Dim: 512x512x3 One perceptron
- 18. 18 Deep learning is a set of machine learning architectures inspired by the structure and function of the human brain 18 Deep learning explainedDeep learning applied 13 X W + b Y IMAGE SIMPLE NEURAL NETWORK Size Price Y = w * X +b OPTIMIZE LOSS FUNCTION Here: Mean Square Error (MSE) HOW DOES THIS APPLY ON IMAGES? Dim: 512x512x3 X X X X … EVERY PIXEL IS AN INPUT… MEANS LOTS OF DATA TO PROCESS IN THE NETWORK Solution: CNN One perceptron
- 19. 19 Deep learning is a set of machine learning architectures inspired by the structure and function of the human brain 19 Deep learning explainedDeep learning applied 14 CNN APPLIED TO IMAGE SEGMENTATION Many hidden layers: deep learning
- 20. 20 In order to obtain CRAE and CRVE in an automated way, deep learning can be applied in two ways (here: overview of the first) 20 SegmentationDeep learning applied 16 CNN APPLIED TO IMAGE SEGMENTATION Backpropagation to alter network weights Manual A/V labeling – GROUND TRUTH Fundus image C O M P A R E Model prediction
- 21. 30th of March, 2017 Thank you for your attention! Ruben Hemelings KU Leuven rhemelings@me.com Contact details Bart Elen VITO bart.elen@vito.be