Deep learning on biomedical images. Ruben Hemelings Graduate VITO KU Leuven. Data Innova)on Summit March, #DIS2017

Transcription

1 Deep learning on biomedical images Ruben Hemelings Graduate VITO KU Leuven Data Innova)on Summit March, #DIS2017

2 Research Automated analysis of blood vessels with deep learning 30th of March Ruben Hemelings

3 Session outline Topics to cover 1 Context Medical and technological motivation 2 Deep learning applied

4 A quick refresher on the anatomy of the human eye I think we all know this Intro 1

5 A quick refresher on the anatomy of the human eye But do you remember this? Intro 1

6 Research proposal Quick overview 1 Fundus photography 2 Vessel extraction 3 Artery/Vein labelling Focus area Automated analysis of blood vessels with deep learning Context Quick overview 1

7 Analysis of the retina offers a non-intrusive way of diagnosing ocular aberrations, but also systemic diseases 1 Ocular diseases 2 Systemic diseases Diabetic retinopathy Cardiovascular disease (CVD) Glaucoma Ocular hypertension Optic disc Alzheimer Atherosclerosis Fovea Rods and cones - zoom Image of the human retina Context Medical motivation 2 7

8 Analysis of the retina offers a non-intrusive way of diagnosing ocular aberrations, but also systemic diseases 1 Ocular diseases 2 Systemic diseases Diabetic retinopathy Hemorrhage Cardiovascular disease (CVD) Glaucoma Ocular hypertension Optic disc Alzheimer Atherosclerosis Fovea Image of the human retina Hard exudate Context Medical motivation 3 8

9 Analysis of the retina offers a non-intrusive way of diagnosing ocular aberrations, but also systemic diseases 1 Ocular diseases 2 Systemic diseases Diabetic retinopathy Narrowing arterioles Cardiovascular disease (CVD) Glaucoma Ocular hypertension Cupping of optic disc Alzheimer Atherosclerosis Fovea Image of the human retina Context Medical motivation 4 9

10 Analysis of the retina offers a non-intrusive way of diagnosing ocular aberrations, but also systemic diseases 1 Ocular diseases 2 Systemic diseases Diabetic retinopathy Narrowing arterioles Cardiovascular disease (CVD) Glaucoma Ocular hypertension Optic disc Alzheimer Atherosclerosis Fovea Narrowing venules Image of the human retina Context Medical motivation 5 10

11 Analysis of the retina offers a non-intrusive way of diagnosing ocular aberrations, but also systemic diseases 1 Ocular diseases 2 Systemic diseases Diabetic retinopathy Narrowing arterioles Cardiovascular disease (CVD) Glaucoma Ocular hypertension Optic disc Alzheimer Atherosclerosis Fovea SIMILAR SYMPTOMS OBSERVABLE IN VASCULATURE Widening venules (Hypertension, atherosclerosis) Narrowing venules (Alzheimer) Image of the human retina Context Medical motivation 6 11

12 Analysis of the retina offers a non-intrusive way of diagnosing ocular aberrations, but also systemic diseases 1 Ocular diseases CRAE = Average artery width 2 Systemic diseases LINKED A/V Ratio CRVE = Average vein width Optic disc Fovea How to compute A/V ratio in automated setting? = Image segmentation problem 12 Context Medical motivation 7

13 Image segmentation methods based on deep learning are currently outperforming other methods PIXEL-WISE CLASSIFICATION COMPETITION TO LABEL 20 TYPES OF OBJECTS, DOMINATED BY DEEP LEARNING ARCHITECTURES Context Technological motivation 8 13

14 But has not been applied on the A/V classification of retinal blood vessels up to now PIXEL-WISE CLASSIFICATION ALREADY APPLIED NOT APPLIED THUSFAR 14 Context Technological motivation 9

15 Deep learning is a set of machine learning architectures inspired by the structure and function of the human brain Price SIMPLE NEURAL NETWORK One perceptron Y = w * X +b X W + b Y IS THIS THE BEST LINE? Size Deep learning applied Deep learning explained 13 15

16 Deep learning is a set of machine learning architectures inspired by the structure and function of the human brain Price SIMPLE NEURAL NETWORK One perceptron Y = w * X +b X W + b Y OPTIMIZE LOSS FUNCTION Here: Mean Square Error (MSE) Size Deep learning applied Deep learning explained 13 16

17 Deep learning is a set of machine learning architectures inspired by the structure and function of the human brain Price SIMPLE NEURAL NETWORK One perceptron Y = w * X +b X W + b Y OPTIMIZE LOSS FUNCTION Here: Mean Square Error (MSE) Size HOW DOES THIS APPLY ON IMAGES? IMAGE Dim: 512x512x3 Deep learning applied Deep learning explained 13 17

18 Deep learning is a set of machine learning architectures inspired by the structure and function of the human brain Price SIMPLE NEURAL NETWORK One perceptron Y = w * X +b X W + b Y OPTIMIZE LOSS FUNCTION Here: Mean Square Error (MSE) Size HOW DOES THIS APPLY ON IMAGES? IMAGE X X X EVERY PIXEL IS AN INPUT MEANS LOTS OF DATA TO PROCESS IN THE NETWORK Solution: CNN Dim: 512x512x3 Deep learning applied Deep learning explained 13 X 18

19 Deep learning is a set of machine learning architectures inspired by the structure and function of the human brain CNN APPLIED TO IMAGE SEGMENTATION Many hidden layers: deep learning Deep learning applied Deep learning explained 14 19

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) CNN APPLIED TO IMAGE SEGMENTATION Model prediction Fundus image Backpropagation to alter network weights C O M P A R E Manual A/V labeling GROUND TRUTH Deep learning applied Segmentation 16 20

21 Thank you for your attention! Contact details Ruben Hemelings KU Leuven Bart Elen VITO 30th of March, 2017