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IIIT Hyderabad
An overview
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IIIT Hyderabad
About me
• Past:• Research Intern at IIIT- Hyderabad.
• Supervisor: Jayanthi Sivaswamy • MS by Research at IIIT-Hyderabad.
• Supervisor: Jayanthi Sivaswamy
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IIIT Hyderabad
About me
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IIIT Hyderabad
About me
• Past:• Research Intern at IIIT- Hyderabad.
• Supervisor: Jayanthi Sivaswamy • MS by Research at IIIT-Hyderabad.
• Supervisor: Jayanthi Sivaswamy
• Present• Started PhD in Fall 2017 at ETS Montreal
• Supervisors: Hervé Lombaert and Christian Desrosiers
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IIIT Hyderabad
A design for an automated Optical Coherence Tomography analysis system
Karthik GopinathAdvisor : Prof. Jayanthi Sivaswamy
CVIT, IIIT Hyderabad
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IIIT Hyderabad
Anatomy of eye
Cross sectional imagingProjection imaging
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IIIT Hyderabad
• 3D representation of retina.• Non-invasive in nature.• Shorter image acquisition time.
Why Optical Coherence Tomography?
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IIIT Hyderabad
Retinal diseasesAge related macular degeneration (AMD)
Vision of a normal person and a person with AMD
Cystoidal macular edema (CME) Glaucoma
Vision of a normal person and a person with CME Vision of a normal person and a person with Glaucoma
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IIIT Hyderabad
Proposed OCT analysis system
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IIIT Hyderabad
Motivation and Challenges in retinal layer segmentation
Pathologies affecting layer morphology
Presence of speckle noise.
Vessel shadows.
Various layer orientation.
• Motivation– Quantifying retinal layer thickness – Understanding progressive health of the retina.
• Challenges
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IIIT Hyderabad
Comparison of retinal layer segmentation algorithms
• Included pre-processing steps:– Denoising.– Vessel shadow removal.– Flattening.
• Sequential segmentation of layers[1].• Pathology dependent methods[3].
• No requirement for any pre-processing.• One-shot solution multi-layer
segmentation.• Robustness to presence of pathologies.• Robustness to imaging systems and
image quality.
Previous works Proposed work
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IIIT Hyderabad
Visualization of different retinal layers
Retinal layer boundaries in OCT B-scans of a) Healthy retina, listed from top to bottom: ILM(Red), NFL/GCL(Green), IPL/INL(blue), INL/OPL(yellow), OPL/ONL(cyan), IS/OS(magenta), RPEin(pink) and RPEout (purple); b) Retina with AMD: ILM(Red), RPEin(Green) and RPEout(Blue).
a b
Original image
Required retinal layer
boundaries
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IIIT Hyderabad
Proposed Architecture
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IIIT Hyderabad
Data and Training Method• Dataset description (Public datasets)
• Training– Two copies:
• Only normal cases Mnorm.• Both normal and pathological cases Mmixed.
– Training and testing set split: 8:2.– Online data augmentation.– Entire network trained in an end-to-end fashion.
Chiu_norm [1] (Normal cases) OCTRIMA3D [2] (Normal cases) Chiu_path[3] (Pathology cases)
110 B-scans from 10 healthy subjects (11 B-scans per subject).
100 B-scans from 10 healthy subjects (10 B-scans per subject).
220 B-scans from 20 subjects(11 B-scans per subject).
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IIIT Hyderabad
Intermediate results
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IIIT Hyderabad
Qualitative results
Comparison with manual (green) segmentation and obtained results.
Comparison with manual (green) segmentation and obtained results.
Mnorm Mmixed
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IIIT Hyderabad
• Pixel-wise mean absolute error/standard deviation:– Mnorm:
– Mmixed:
Quantitative results
Mnorm Chiu_norm OPTIMA 3D
Inter-marker error
[1] Ours Inter-marker error
[2] Ours
MAE 2.25/1.08 1.39/0.48 1.34/0.44 1.44/0.47 1.00/0.33 1.30/0.41
Mmixed Chiu_norm OPTIMA 3D Chiu_path
Inter-marker error
[1] Ours Inter-marker error
[2] Ours Inter-marker error
[3] Ours
MAE 2.14/1.19 1.18/0.39 1.16/0.46 1.34/0.45 0.89/0.348 0.92/0.31 1.79/0.62 1.71/0.76 1.78/0.78
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IIIT Hyderabad
Proposed OCT analysis system
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IIIT Hyderabad
• Glaucoma– Second leading cause of blindness in the world.– Cause of irreversible blindness.
• Multiple Indicators.– From projection imaging.– From structural image.
• We attempt glaucoma diagnosis from functional imaging.
• Our proposal: Detect glaucoma via vascular health assessment.• To get information about functioning of the vessel network
– New modality - OCT Angiography (3D)• non-invasive technique • vasculature information available at various retinal layers
Glaucoma diagnosis at volume level
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IIIT Hyderabad
The different enface images in relation to a cross-sectional OCT Angiography slice.
Data visualization from OCT Angiography
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IIIT Hyderabad
Proposed Method
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IIIT Hyderabad
Choroid Disc Angioflow image Eight sectors in ROI
Region of interest extraction
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IIIT Hyderabad
Capillaries at different layers
Capillaries at different layers in ROI
Feature extraction
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IIIT Hyderabad
Features
Features
Linear SVMClassifier
Training and Testing
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IIIT Hyderabad
• OCTA images from an Optovue scanner collected from a Anand Eye hospital.
• Dataset
• Data per eye/volume– A OCTA volume– Four angioflow images at different layers (choroidal disc, nerve head,
RPC and the vitreous layers). – Ground truth from one expert
Experiments and results
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IIIT Hyderabad
RNFL thickness for a Glaucomatous and a Normal case across sectors (left). Capillary density (CD) variation for a Glaucomatous and a Normal case across sectors. The average CD across 4 layers is shown for each sector
RNFL alone CD alone RNFL + CD for all layers
Mean (Std. Dev. )
Mean (Std. Dev. ) Mean (Std. Dev. )
Sensitivity 0.44 (0.17) 0.83 (0.18) 0.94(0.13)
Specificity 0.87 (0.13) 0.79 (0.14) 0.91(0.10)
Accuracy 0.76 (0.12) 0.80 (0.09) 0.92(0.08)
Experiments and results
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IIIT Hyderabad
Proposed OCT analysis system
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IIIT Hyderabad
• Different disease indicators:– AMD case - drusen (aberration in bright layer).– CME case - retinal cyst/fluid filled regions (dark regions).
Normal case
Slice level disease diagnosis
AMD case CME case
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IIIT Hyderabad
Comparison of disease classification algorithms
• Statistical analysis of layer thickness. [4]
• Morphological and textural features with decision tree. [5]
• Histogram of Oriented Gradients (HOG) descriptors + SVM. [6]
• An automated CNN based solution• Disease classification using extremal
representations.
Previous works Proposed work
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IIIT Hyderabad
Motion blur due to moving object Motion blur due to moving source
Motion blur in natural setting
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IIIT Hyderabad
The phantom image The phantom image translated in the vertical direction
Generalised motion pattern image in extremal
representation space
}
}-15 -10 -5 0 5 10 15
-15 -10 -5 0 5 10 15
max
min
Coalescingfunction
Synthesizing motion blur from Generalised motion pattern (GMP) [8][9]
EXmax
EXmin
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IIIT Hyderabad
Proposed Architecture for disease diagnosis
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IIIT Hyderabad
Experiments and Results
• Datasets:– Train set : 3500 OCT slices – Test set : 1995 OCT slices
• Performance of the proposed system
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IIIT Hyderabad
Proposed OCT analysis system
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IIIT Hyderabad
Motivation and Challenges in abnormality segmentation
Variable shape, size, appearance and arbitrary locations
Inter-scanner variability in images
• Motivation– Treatment planning for the patients. – Localizing and quantifying them will aid in accurate surgical interventions.
• Challenges
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IIIT Hyderabad
Comparison of abnormalitysegmentation algorithms
• Semi-automatic. [9]• Thresholding and boundary tracing. [10]• Graph-search/graph-cut based algorithm. [11]• Voxel classification approaches. [12]
• Unique representation for the OCT data, based on motion patterns. [7][8]
• Selective enhancement of cysts with CNN.• Combining both 2D and 3D information in CNN.• Vendor independent and robust system for
detecting and localizing cysts.
Previous works Proposed work
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IIIT Hyderabad
The proposed pipelinefor abnormality segmentation
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IIIT Hyderabad
Pre-Processing for abnormality segmentation
The original image
The ROI image The denoised image The LOI image
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IIIT Hyderabad
Selective enhancement fromGeneralised motion pattern
Generalised motion pattern
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IIIT Hyderabad
Proposed Architecture
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IIIT Hyderabad
Evolution of the mapping function over epochs
(a) ROI input, (b) Ground truth: Grader1∩Grader2, the output of CNN is shown as a probability (heat) map at (c). 10th, (d) 50th, (e) 100th, (f) 200th, (g) 300th, (h) 500th, (i) 900th, (j) 1420th epochs.
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IIIT Hyderabad
• The output of the trained is thresholded to obtain a binary map.
• Multiplied ROI image with binary map.
• K-means clustering is applied on this product image.
– Cysts
– False positives region
– Background.
Segmentation
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IIIT Hyderabad
• Datasets
Experiments and Results
OCSC dataset (Public)
DME dataset (Public)
Anand Eye Institute dataset (Private)
15 training and 15 testing volumes with varying number of (5-200) B-scans from 4 different scanners.
10 volumes with 61 B-scans.10 volumes with varying number of (30-51) B-scans
GT from two experts GT markings for 11 B-scans per volume (not continuous).
GT for every 5th slice from one expert.
Note: Training is done only on OCSC train set.
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IIIT Hyderabad
Qualitative results
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IIIT Hyderabad
Qualitative results
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IIIT Hyderabad
Effect of the number of GMPs
Segmentation performance as a function of the number (K) of GMPs.
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IIIT Hyderabad
Effect of the number of translation in each GMP
Segmentation performance as a function of the extent (N) of translation in each GMP.
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IIIT Hyderabad
• System performance is assessed by computing the Dice Coefficient.
• Mean DC values for different variants of the proposed CNN model on the OCSC test set
• Mean DC values for different inputs and variants for selective enhancement
Pixel level abnormality segmentation
Input 2D Proposed architecture
Proposed architecture + K-means clustering
DC 0.56 0.64 0.69
Method OCSCTest set
DME dataset
AEI dataset
U-net [15] with ROI as input 0.58 0.51 0.69
U-net with GMP as input 0.61 0.55 0.71
Proposed architecture + K-means clustering 0.69 0.67 0.79
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IIIT Hyderabad
• We developed an anatomy segmentation module– Parameter independent.– can handle both normal and AMD affected retina.
• We explored the use of additional angio information for glaucoma classification.
• We proposed a slice level multiple disease classification using extremal representation and CNN.
• We presented an algorithm for localizing/segmenting retinal.– Generic approach independent of scanners.– Use of CNN to learn a mapping function for selective enhancement of cysts.
Summary
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IIIT Hyderabad
• Anatomy extraction:– Segmentation of retinal layers in presence of pathologies like cysts.– Segmentation of retinal layer around optic disc (OD).
• Classification of diseases at volume level:– Investigate the new imaging modality OCTA for the effect of vasculature on other
retinal disease.
• Classification of diseases at volume level:– Multiple disease diagnosis.– Grading of the diseases according to its stages.
• Abnormality extraction:– Enhance the existing segmentation scheme by developing an end to end CNN
based module.
Future work
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IIIT Hyderabad
• Domain knowledge assisted cyst segmentation in OCT retinal images.
Karthik Gopinath and Jayanthi Sivaswamy.
arXiv preprint arXiv:1612.02675 (2016). (Accepted for oral presentation in MICCAI 2015 OPTIMA challenge).
• Automatic glaucoma assessment from angio-OCT images.
Karthik Gopinath, Jayanthi Sivaswamy and Tarannum Mansoori.
IEEE 13th International Symposium on Biomedical Imaging (ISBI) 2016, Prague, Czech Republic
• A deep learning framework for segmentation of retinal layers from OCT images.
Karthik Gopinath*, Samrudhdhi B*, and Jayanthi Sivaswamy.
The 4th Asian Conference on Pattern Recognition (ACPR), November 2017, Nanjing, China
• Under Review in Journal of Biomedical and Health Informatics
Automated segmentation of retinal cysts from Optical Coherence Tomography volumes.
Karthik Gopinath and Jayanthi Sivaswamy.
*equal contribution
Related Publications
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IIIT Hyderabad
References[1] S. J. Chiu et al., “Automatic segmentation of seven retinal layers in sdoct images congruent with expert manual segmentation,” Optics express, vol. 18, no. 18, pp.
19413–19428, 2010.[2] J. Tian et al., “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PloS one, vol. 10, no. 8, p. e0133908, 2015.[3] S. J. Chiu et al., “Validated automatic segmentation of amd pathology including drusen and geographic atrophy in sd-oct images,” Investigative ophthalmology & visual
science, vol. 53, no. 1, pp. 53–61, 2012.[4] G. M. Somfai et al., “Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes,” BMC bioinformatics, vol. 15, no. 1, p. 1, 2014.[4] S. Boyd
and L. Vandenberghe, Convex optimization. Cambridge university press, 2004.[5] R. Koprowski, S et al., “Automatic analysis of selected choroidal diseases in oct images of the eye fundus,” Biomedical engineering online, vol. 12, no. 1, p. 1, 2013.[6] P. P. Srinivasan et al., “Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images,”
Biomedical optics express, vol. 5, no. 10, pp. 3568–3577, 2014.[7] K. S. Deepak et al., “Detection and discrimination of diseaserelated abnormalities based on learning normal cases,” Pattern Recognition, vol. 45, no. 10, pp.
3707–3716, 2012.[8] K. S. Deepak and J. Sivaswamy, “Automatic assessment of macular edema from color retinal images,” IEEE Transactions on Medical Imaging, vol. 31, no. 3, pp.
766–776, 2012.[9] D. C. Fernandez, “Delineating fluid-filled region boundaries in optical coherence tomography images of the retina,” IEEE transactions on medical imaging, vol. 24, no. 8,
pp. 929–945, 2005.[10]G. R. Wilkins et al., “Automated segmentation of intraretinal cystoid fluid in optical coherence tomography,” IEEE Transactions on Biomedical Engineering, vol. 59, no.
4, pp. 1109–1114, 2012.[11] X. Chen et al., “Threedimensional segmentation of fluid-associated abnormalities in retinal oct: probability constrained graph-search-graph-cut,” IEEE transactions on
medical imaging, vol. 31, no. 8, pp. 1521–1531, 2012.[12] X. Xu et al.,“Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical oct data,” IEEE transactions on medical
imaging, vol. 34, no. 7, pp. 1616–1623, 2015.[13] Dataset, “Dme dataset from duke univerity.”[14] J. Wu et al., “Multivendor spectral-domain optical coherence tomography dataset, observer annotation performance evaluation, and standardized evaluation
framework for intraretinal cystoid fluid segmentation,” Journal of Ophthalmology, vol. 2016, 2016.[15]O. Ronneberger et al., “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and
Computer-Assisted Intervention, pp. 234–241, Springer, 2015.
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IIIT Hyderabad
We thank
Anand Eye Institute, Hyderabad(for data and ground truth)
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IIIT Hyderabad
Thank you