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Page 1: Image Processing for Optical Coherence Tomography · Image Processing for Optical Coherence Tomography ... $250 $110 $280 $110 $250 ... Image Processing for Optical Coherence Tomography

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Image Processing for Optical Coherence Tomography

Jonathan Oakley and Daniel Russakoff, Voxeleron LLC

HISB 2011, July 29th, 2011

Overview

• Optical Coherence Tomography (OCT) – Brief History – Overview of the Modality

• Methods and Applications in Ophthalmology – Image pre-processing – Layer Segmentation

• Graph-based – 1d – Graph Cuts

• Shape-based

– Optic Nerve Segmentation – Image Registration

• Other application domains of OCT • Future trends

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OPTICAL COHERENCE TOMOGRAPHY

5mm

http://www.lantislaser.com/home.asp

http://obel.ee.uwa.edu.au/research/oct/intro/

The 6th Imaging Modality

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• OCT: – Based on light back-reflected from within a

medium – Optical analogue of Ultra-Sound – Is Non-Invasive and contactless – Is an Interferometric technique – Generates high-resolution, cross-sectional

images – Applicable to semi-transparent materials

OCT in a Nutshell

Optical Coherence Tomography

http://obel.ee.uwa.edu.au/research/oct/intro/

• At the detector we have a signal only when zref=zeye

• This is known as coherence gating • The axial resolution is limited by the bandwidth of the light source

Rastering builds up a B-scan. Multiple B-scans build up a volume.

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• It can be shown that the measured spectrum of the interferometer output contains the same information as an axial scan of the reference arm. The map of optical reflectivity versus depth is obtained from the interferometer output spectrum via a Fourier Transform

Spectral/Fourier Domain OCT

• Biomedical Examples: – Ophthalmology – Endoscopy – Dermatology – Dentistry – Microscopy

• Material Sciences Examples: – Any layered structure of interest

• Mulitlayered foils, foods, paintings and artwork, printed electronic circuits, etc

Applications

http://www.octnews.org, http://www.recendt.at/517_ENG_HTML.php

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Huang et al. Science, 1991

http://www.coollab.net/fileadmin/coollab_upload/coollab/docs/huang-new_OCT_ophthalmology.pdf

A Comparison of Optic Nerve Head Morphology Viewed by Spectral Domain Optical Coherence Tomography and by Serial Histology Strouthidis et al. IOVS, March 2010, Vol. 51, No. 3

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OPHTHALMOLOGY AND OPTICAL COHERENCE TOMOGRAPHY

Eye Anatomy 101

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Light Anatomy:

http://telemedicine.orbis.org/bins/home.asp

Leading causes of blindness in the US

www.allaboutvision.com & http://www.diabetes.org/diabetes-basics/diabetes-statistics/

Approximately 2.5 million Americans estimated to have Glaucoma.

About 1.75 million U.S. residents currently have advanced age-related macular degeneration (AMD) with associated vision loss, with that number expected to grow to almost 3 million by 2020.

40 to 45 percent of Americans diagnosed with diabetes have some stage of diabetic retinopathy. That’s ~10 million people. It can lead to blindness.

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Ophthalmology

• The patient positions their chin on a chin rest

• The operator acquires the image once the scan patterned is positioned

• The image is captured based on the back scattered light

• Are fundamental in the use of the instrument

• Minimal requirements are:

– Layer segmentation

– Image Registration

– Motion Correction

Analysis Algorithms

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IMAGE PROCESSING IN OPHTHALMIC OCT

• Commercial – Matlab

• Special purpose toolboxes • Academic source code

– Intel Performance Primitives (IPP)

• Open Source – VL feat – Open CV – ImageJ – Generic Image Library – Insight Tool Kit

What software is available

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• Total Retinal Thickness is perhaps the most fundamental analysis method in ophthalmic OCT images

• Fortunately, this measurements is based on the two most obvious layers; the ILM and the RPE:

Retinal Layer Segmentation

http://www.meditec.zeiss.com/C125679E00525939/ContainerTitel/CirrusOCT/$File/retinal-measurements.pdf

Giani et al. ARTIFACTS IN AUTOMATIC RETINAL SEGMENTATION USING DIFFERENT OPTICAL COHERENCE TOMOGRAPHY INSTRUMENTS, RETINA, THE JOURNAL OF RETINAL AND VITREOUS DISEASES, 2010 Apr;30(4):607-16.

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Dry Age-Related Macular Degeneration

21

Giovanni Gregori, Ph.D., of Bascom Palmer Eye Institute - http://www.ophmanagement.com

Gregori et al., Spectral Domain Optical Coherence Tomography Imaging of Drusen in Nonexudative Age-Related Macular Degeneration, Ophthalmology, Volume 118, Issue 7 , Pages 1373-1379, July 2011

IMAGE PRE-PROCESSING Noise reduction

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Speckle Noise

• Due to constructive and destructive interference

• Reduces contrast and makes boundaries between highly scattering structures difficult to resolve

• Approximately follows a Rayleigh distribution

Schmitt et al., SPECKLE IN OPTICAL COHERENCE TOMOGRAPHY, JOURNAL OF BIOMEDICAL OPTICS 4(1), 95–105 (JANUARY 1999)

• Median Filter • Sticks algorithm

– Directional Filtering • J. Rogowska and M. E. Brezinski, “Evaluation of

the adaptive speckle suppression filter for coronary optical coherence tomography imaging,” IEEE Trans. Med. Imaging, 19, 1261–6 (2000).

• Anisotropic Diffusion Filtering – Edge Preserving Smoothing

• Yu and Acton, Speckle Reducing Anisotropic Diffusion, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 11, NO. 11, NOVEMBER 2002

Speckle Noise Reduction

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Median Filtering

Data courtesy of Robert Chang MD, Stanford School of Medicine

• Macular Segmentation with Optical Coherence Tomography, Investigative Ophthalmology & Visual Science, June 2005, Vol. 46, No. 6

Ishikawa et al.

Uses median filter and A-scan alignment

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Sticks Algorithm

http://www.mathworks.com/matlabcentral/fileexchange/14862-sticks-filter/content/sf.m

Data courtesy of Robert Chang MD, Stanford School of Medicine

Pre-Processing & Down-sampling

Data courtesy of Robert Chang MD, Stanford School of Medicine

Hu et al.

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INTRA-RETINAL LAYER SEGMENTATION

Two key approaches are coming to the fore

• Graph-based methods: – Mathematical advances in the field of graph

theory has led to optimization techniques applicable to N-D graphs (or images)

• Statistical shape models: – Techniques to represent prior knowledge of

an object of interest’s shape/appearance • Constrains optimization space

• Fills in noisy data

More Recent Developments in Image Processing

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GRAPH-BASED 1d Graph Traversal

Images cast as Graphs

Graph G=(V,E)‏

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Dijkstra’s Algorithm

http://www.algolist.com/Dijkstra's_algorithm

1. Set source node as current node, set to 0, set all others to infinity. Mark all nodes as unvisited

2. For each unvisited node adjacent to the current node, do: If the value of the current node + edge is less than the value of the adjacent node, change the value to this value. 3. Set current node to visited. If there are still some unvisited nodes, set that with the smallest value to the current node, and go to 2. Else, finish.

Dynamic Programing

Seattle $200

San Jose $180

San Diego $350

Duluth $180

St Louis $170

Dallas $150

Boston $240

New York $300

Miami $220

i=0..2

j=0..2

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Create Minimum Cumulative Costs

Seattle $200

San Jose $180

San Diego $350

Duluth $360

St Louis $350

Dallas $330

Boston $590

New York $630

Miami $550

i=0..2

j=0..2

Miami $550

Dallas $330

San Jose $180

San Jose $180

St Louis $170

Dynamic Programing

Seattle $200

San Diego $350

Duluth $180

Dallas $150

Boston $240

New York $300

Miami $220

$150 $130

$180

$180

$190

$250

$110

$280

$110

$250

$290

$120

$210

$100

i=0..2

j=0..2

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San Jose $180

Boston $900

Create Minimum Cumulative Costs

Seattle $200

San Diego $250

Duluth $530

St Louis $460

Dallas $580

New York $860

Miami $890

$250

$110

$210

Cheapest traversal from east coast to west coast?

$150

i=0..2

j=0..2 $130

$100 San Jose $180

San Jose $180

St Louis $170

New York $300

• Additional weighting can be added to penalize the traversal if it deviates from a straight line

• Typically, this is done by adding a smoothing term, h(l), with associated costs: – Where C is our cumulative cost image, and c our cost

image

• This actually allows us to change the bias “on the

fly” pushing the segmentation path upwards or downwards as conditions indicate

Biasing the Lowest Cost Path

Timp & Karssemeijer, A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography, Med. Phys. 31 .5., May 2004

𝐶 𝑖 + 1, 𝑗 = 𝑚𝑖𝑛−𝑚<𝑙<𝑚𝐶 𝑖, 𝑗 + 𝑙 + 𝑐 𝑖, 𝑗 + 1 + ℎ(𝑙)

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The Smoothing Term

• The smoothing term, h(l), affords you a lot of control over the path taken

• This is very important in retinal layer segmentation

• It’s width, m is often called the size of the margin

• It determines the architecture of your graph Margin = 2:

1 2 3 4 5 6 7 8 90

20

40

60

80

100

120

140

160

180

Deviation from Horizontal Path

Additative P

enaliz

ation

Margin Term

Margin = 2

Margin = 3

Margin = 4

• Uses Dijkstra’s algorithm to traverse the cost images data

• Margin set to 1, with no smoothing penalty

• Cost images based on signed edges: – Dark to light image for segmenting a darker

layer above a lighter layer – Light to dark image for segmenting a lighter

layer above a darker layer

Chiu et al., 2010

Chiu et al. Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation, Vol. 18, No. 18 / OPTICS EXPRESS

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• Dijkstra’s algorithm requires explicit setting of the start and end nodes – This requires adding columns either end

of the image with zero cost

– Alternative graph-traversal methods mitigate this problem

• Image must be pre-flattened to the curvature of the retina

Chiu et al., 2010

Chiu et al., 2010

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• Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming, 1 June 2011 / Vol. 2, No. 6 / BIOMEDICAL OPTICS EXPRESS 1524

LaRocca et al., 2011

LaRocca et al.

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LaRocca et al.

Yang et al. “Automated layer segmentation of macular OCT images using dual-scale gradient information”, 27 September 2010 / Vol. 18, No. 20 / OPTICS EXPRESS 21293

Yang et al. (Topcon)

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• The original image is processed to create a series of cost images

• Different layers are then segmented such that the data is continually reduced

• Graph architecture not defined

Yang et al. (Topcon)

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• Pros: – Efficient global optimization tools

– Current state of art

• Cons: – Only suitable for 1d structures

– Can’t handle bubbles or object boundaries in volumetric data

Dynamic Programming – Shortest Paths

GRAPH-BASED N-d Graph-Cuts

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n-links

s

t a cut hard constraint

hard constraint

Minimum cost cut can be computed in polynomial time

(max-flow/min-cut algorithms)

22exp

pq

pq

Iw

pqI

Graph-cuts – Boykov-Jolly 2001

http://www.csd.uoc.gr/~komod/ICCV07_tutorial/

pqw

n-links

s

t a cut )(tDp

)(sDp

Incorporating prior information

Suppose are given “expected” intensities

of object and background

ts II and 22 2/||||exp)( s

pp IIsD

22 2/||||exp)( t

pp IItD

http://www.csd.uoc.gr/~komod/ICCV07_tutorial/

Graph-cuts – Boykov-Jolly 2001

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Garvin et al., 2008

Garvin et al. Intraretinal Layer Segmentation of Macular Optical Coherence Tomography Images Using Optimal 3-D Graph Search, IEEE TMI, Vol. 27, No. 10, October 2008

Li, et al. Optimal Surface Segmentation in Volumetric Images – A Graph-Theoretic Approach , IEEE PAMI, Vol. 28, No. 1, January 2006

Garvin et al., 2009

Garvin et al. Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images, IEEE Trans Med Imaging, VOL. 28, NO. 9, September 2009

Cost functions are developed using learned thickness distributions:

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STATISTICAL SHAPE MODELS

Shape modeling overview

Image: Cootes, et al.

• Prior knowledge of a structure or object can be learned and applied as a constraint

• Learn typical shapes

• Save time optimizing

– Search only plausible shapes

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• Robust segmentation of intra-retinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis

Kajić et al. Vol. 18, No. 14 / OPTICS EXPRESS

Learn typical shapes /textures for layers on B-scans

– 26 points per layer, 8 layers per B-scan (208 total)

– 4 texture features per layer (32 total)

– Dimensionality reduction (208 -> 12, 32 -> 2)

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CUP & DISC SEGMENTATION Example OCT Structure Segmentation Algorithms

Cup and Disc Segmentation

60

The Cup and Disc make up the measurable parts of the Optic Nerve Head (ONH)

In the figure, the arrows point to: 1. End of RPE 2. Choroid and (3) sclera extend

past the RPE to form a halo 3. The margin of Bruch’s membrane

appears to extend past the scleral margin

4. Bruch’s Endpoint

Knighton et al. “Structure of the Optic Nerve Head as Addressed by Spectral-Domain Optical Coherence Tomography”, ARVO 2006.

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Xu, et al. 2009

• Segment the disc in 2d using an active contour

• Very difficult to choose appropriate parameters

• Likely to lock onto first feasible (local) result

61 http://www.gig.pitt.edu/Research3.html

Lee et al., 2010 Based on layers segmented within the OCT volume

Lee et al. Segmentation of the Optic Disc in 3-D OCT Scans of the Optic Nerve Head. IEEE Trans. Med. Imaging 29(1): 159-168 (2010)

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Lee et al., 2010

• They axially derive features within the 3d volume, such that the areas of interest can be classified

63

• Moved to a graph-based approach, where the disc was explicitly segmented in a 2d image using a graph-based method

• The cup was then defined as the intersection of the plane with the vitreous interface

Hu et al., 2010

Hu et al., Automated Segmentation of Neural Canal Opening and Optic Cup in 3-D Spectral Optical Coherence Tomography Volumes of the Optic Nerve Head”, Invest. Ophthalmol. Vis. Sci..2010; 0: iovs.09-4838v1-iovs.09-4838

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• Create a 2d image from the OCT volume by integrating in Z

• Restricting the integration range helps improve contrast at the disc

• Then segment the 2d image

Hu et al., 2010

Z-direction

Sum

Data courtesy of Robert Change MD, Stanford School of Medicine

From Topcon brochure

Hu et al., 2010

• Segmentation performed on Cost Images in Polar Space

Polar Transform

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• Graph Algorithmic Techniques for Biomedical Image Segmentation

• Ophthalmic image analysis – 3D – Including segmentation of 11 layers in

macular OCT, and optic nerve head segmentation, ONH layers, deep layers

– Prof. Xiaodong Wu, Ph.D. – Prof. Mona K. Garvin, Ph.D. – Prof. Milan Sonka, Ph.D.

Was a tutorial at Miccai 2010

• US Provisional Application for Patent. S. Farsiu, X.T. Li, S.J. Chiu, P. Nicholas, C.A. Toth, J.A. Izatt. “Automated Segmentation of Layered Structures Such as Retinal Layers Using Graph Cuts.” Submitted January, 2011. DU3383PROV.

Patent Applications in this Domain Juan Xu, Hiroshi Ishikawa, Gadi Wollstein, Joel

S. Schuman. Automated Assessment of Optic Nerve Head with Spectral Domain Optical Coherence Tomography, USSN12/427,184, April 2009.

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• Aside from Intellectual Property issues…. • Segmentations can take around an hour

– Clinical use requires << 10 seconds per volume

• Adoption requires clinical* studies verifying efficacy (accuracy) – FDA for claims and then Sales and Marketing will require

these

• Adoption requires clinical* studies verifying repeatability – Again to support claims to the FDA but also to sell the

instrument

Commercial Adoption

*In essence, this means clinical publications, where the algorithm is of less interest, but the clinical outcome is all important.

• Optovue Inc., were the first to market with an automated intra-retinal segmentation algorithm

• The layer of interest being the posterior of the IPL

• This has value in Glaucoma, where the Ganglion Cell Layer thins

Optovue Inc.

http://www.ophthalmologyweb.com/FeaturedArticle.aspx?spid=23&aid=337

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Saidha et al., Brain 2010

GC-IP thickness is the thickness between the yellow and magenta lines.

More recently, Carl Zeiss Meditec have developed their own inner retina segmentation algorithms

• Has already generated three journal publications: – “Primary retinal pathology in multiple sclerosis as

detected by optical coherence tomography”, Brain, Accepted October 14, 2010

– Since then, another Brain publication has been accepted as well as a submission to the Journal of Multiple Sclerocis

• Algorithm available commercially later this year – Carl Zeiss Meditec, Cirrus version 6.0

Saidha et al., Brain 2010

http://brain.oxfordjournals.org/content/134/2/518.short

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COMMERCIAL ADOPTION Cup & Disc Segmentation Algorithms

Cup & Disc Segmentation

http://www.oct-optovue.com/images/rnfl.jpg

Optovue Inc., were again the first for the new OCT devices:

Heidelberg Cup & Disc Segmentation is based on confocal imaging

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Cup & Disc Segmentation

Courtesy of Robert Chang MD, Stanford University

Followed by Carl Zeiss Meditec Inc.

REGISTRATION Is change pathological?

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• Registration useful for

– Change analysis (intra-modality)

– Image fusion (inter-modality)

• Algorithms

– Landmark-based

– Intensity-based

Algorithms for Ophthalmic Image Registration

• Volumes are typically registered in 2d only

• 3d registration is an area of active research

Change analysis: intensity-based

Vermeer et al. “A model based method for retinal blood vessel detection”, Computers in Biology and Medicine 34 (2004) 209–219 http://www.tecn.upf.es/~afrangi/articles/miccai1998.pdf

Create 2d image

Limit integration

range

Generate and register vessel maps

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Baseline VA 20/30+2

http://www.visioncareprofessional.com/ereader/zeiss/1/czm.pdf

1.5 month post Avastin #4 VA 20/20

http://www.visioncareprofessional.com/ereader/zeiss/1/czm.pdf

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Change Analysis Software

Topcon (left), Zeiss (right)

Combination of Topcon 3D OCT & FA/FAF/ICG images and more Autofluorescence, fluorescence angiography and indocyanine green image is simply imported to allow pin point registration to aid the diagnosis of RPE and choroidal changes

Fusion: automatic

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• OCT / fundus images

– Strong landmarks

– Helps correct OCT artifacts

Fusion: landmark-based

http://www.springerlink.com/content/386552t20738t268/

http://www.meditec.zeiss.com/88256DE3007B916B/0/D023F17419FBEBFDC12576E4003985F6/$file/cirrus_4-0_en.pdf

Fusion: Selective Pixel ProfilingTM

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OTHER APPLICATION DOMAINS

• Rapidly pushing IVUS out of the market for coronary artery pathology assessment – 10x better axial resolution and 3x faster

• Can accurately measure thickness of fibrous caps

(vulnerable plaque) – Assessment of risk of rupture

Cardiology

OCT

IVUS

Image: Patwari, et al., Am. J. Cardiol. 2000

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• Image processing issues:

– Lumen segmentation

– Plaque characterization

89

Image: van Soest et al., J. Biomed. Opt. 2010

Cardiology

• Very useful for certain pathologies

• Good agreement with histology

• Limited by depth penetration (<2mm)

Dermatology

Images: Gambichler, et al. 2011

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• OCT is first modality in dentistry to do high-res imaging both “hard” tissue (teeth) and “soft” tissue (gums)

– Detects decay before it shows up on X-ray

Dentistry

Images: www.LantisLaser.com

• OCT can be used for non-destructive testing of any layered materials

– Art (right)

– Chemical polishes

– Foils

– Bonding quality

Image: Liang, et al., 2011

Non-medical

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FUTURE TRENDS

• Steady evolution of the hardware

– Faster cameras

– Different wavelengths

– Combo modalities

– Lower costs, etc.

– Cheaper components

Hardware

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“Ultrahigh speed 1050nm swept source / Fourier domain OCT retinal and anterior segment imaging at 100,000 to 400,000 axial scans per second”, 13 September 2010 / Vol. 18, No. 19 / OPTICS EXPRESS 20031 Benjamin Potsaid, Bernhard Baumann, David Huang, Scott Barry, Alex E. Cable, Joel S. Schuman, Jay S. Duker, and James G. Fujimoto

Fig. 7. (A) OCT fundus image of 3D volume acquired at 100kHz with 500x500 axial scans over 6mmx6mm (2.6 sec). (B) 100kHz cross sectional image. (C) 3D volume rendering of 100kHz data (Media 1). (C) OCT fundus image of 3D volume acquired at 200kHz with 700x700 axial scans over 6mmx6mm (2.6 sec). (D) 200kHz cross sectional image. (E) 3D volume rendering of 200kHz data. Images are cropped in depth to span 2mm.

• Image Management Systems – Better electronic record systems and

management

– “Open Architectures”

• Image processing algorithms – The low hanging fruit in ophthalmology is

perhaps gone

• But…

Software

http://www.retinalphysician.com/article.aspx?article=103653

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– Sophisticated methods are too slow, and not, therefore, clinically applicable

– The fast methods are not sophisticated enough to be accurately applied across all disease states

– Ideally you would want to take the best of both worlds from all algorithms we have seen

– A marriage of shape and graph-based methods can leverage both

– Careful reductionist techniques may yet make them execute at clinically realistic speeds

– Lower quality devices will soon be ubiquitous, so the analysis tools offered will be a key differentiator

– Ultimately clinical usage is based on analysis performance

Analysis Algorithms

[email protected]

[email protected]

Thank you!

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References

• http://obel.ee.uwa.edu.au/research/oct/intro/ • http://www.lantislaser.com/home.asp • http://obel.ee.uwa.edu.au/research/oct/intro/ • http://www.octnews.org • http://www.recendt.at/517_ENG_HTML.php • http://www.coollab.net/fileadmin/coollab_upload/coollab/docs/huang-

new_OCT_ophthalmology.pdf

• A Comparison of Optic Nerve Head Morphology Viewed by Spectral Domain Optical Coherence Tomography and by Serial Histology

• Strouthidis et al. IOVS, March 2010, Vol. 51, No. 3 • http://telemedicine.orbis.org/bins/home.asp • www.allaboutvision.com & http://www.diabetes.org/diabetes-basics/diabetes-

statistics/ • http://www.meditec.zeiss.com/C125679E00525939/ContainerTitel/CirrusOCT/$Fil

e/retinal-measurements.pdf

References

• Giani et al. ARTIFACTS IN AUTOMATIC RETINAL SEGMENTATION USING DIFFERENT OPTICAL COHERENCE TOMOGRAPHY INSTRUMENTS, RETINA, THE JOURNAL OF RETINAL AND VITREOUS DISEASES, 2010 Apr;30(4):607-16.

• http://www.ophmanagement.com • Gregori et al., Spectral Domain Optical Coherence Tomography Imaging of Drusen

in Nonexudative Age-Related Macular Degeneration, Ophthalmology, Volume 118, Issue 7 , Pages 1373-1379, July 2011

• Schmitt et al., SPECKLE IN OPTICAL COHERENCE TOMOGRAPHY, JOURNAL OF BIOMEDICAL OPTICS 4(1), 95–105 (JANUARY 1999)

• J. Rogowska and M. E. Brezinski, “Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging,” IEEE Trans. Med. Imaging, 19, 1261–6 (2000).

• Yu and Acton, Speckle Reducing Anisotropic Diffusion, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 11, NO. 11, NOVEMBER 2002

• Macular Segmentation with Optical Coherence Tomography, Investigative Ophthalmology & Visual Science, June 2005, Vol. 46, No. 6

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References

• http://www.mathworks.com/matlabcentral/fileexchange/14862-sticks-filter/content/sf.m

• http://www.algolist.com/Dijkstra's_algorithm • Timp & Karssemeijer, A new 2D segmentation method based on dynamic

programming applied to computer aided detection in mammography, Med. Phys. 31 .5., May 2004

• Chiu et al. Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation, Vol. 18, No. 18 / OPTICS EXPRESS

• Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming, 1 June 2011 / Vol. 2, No. 6 / BIOMEDICAL OPTICS EXPRESS 1524

• Yang et al. “Automated layer segmentation of macular OCT images using dual-scale gradient information”, 27 September 2010 / Vol. 18, No. 20 / OPTICS EXPRESS 21293

• Boykov-Jolly 2001 • http://www.csd.uoc.gr/~komod/ICCV07_tutorial/

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• Garvin et al. Intraretinal Layer Segmentation of Macular Optical Coherence Tomography Images Using Optimal 3-D Graph Search, IEEE TMI, Vol. 27, No. 10, October 2008

• Garvin et al. Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images, IEEE Trans Med Imaging, VOL. 28, NO. 9, September 2009

• Kajić et al. Vol. 18, No. 14 / OPTICS EXPRESS • Knighton et al. “Structure of the Optic Nerve Head as Addressed by Spectral-

Domain Optical Coherence Tomography”, ARVO 2006. • http://www.gig.pitt.edu/Research3.html • Lee et al. Segmentation of the Optic Disc in 3-D OCT Scans of the Optic Nerve

Head. IEEE Trans. Med. Imaging 29(1): 159-168 (2010) • Hu et al., Automated Segmentation of Neural Canal Opening and Optic Cup in 3-D

Spectral Optical Coherence Tomography Volumes of the Optic Nerve Head”, Invest. Ophthalmol. Vis. Sci..2010; 0: iovs.09-4838v1-iovs.09-4838

• http://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdf

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References

• http://www.revoptom.com/content/i/796/c/14792/ • http://www.ophthalmologyweb.com/FeaturedArticle.aspx?spid=23&aid=337 • http://brain.oxfordjournals.org/content/134/2/518.short • http://www.oct-optovue.com/images/rnfl.jpg

• Vermeer et al. “A model based method for retinal blood vessel detection”, Computers in Biology and Medicine 34 (2004) 209–219

• http://www.tecn.upf.es/~afrangi/articles/miccai1998.pdf • http://www.visioncareprofessional.com/ereader/zeiss/1/czm.pdf • http://www.springerlink.com/content/386552t20738t268/ • http://www.meditec.zeiss.com/88256DE3007B916B/0/D023F17419FBEBFDC1257

6E4003985F6/$file/cirrus_4-0_en.pdf • Images: Patwari, et al., Am. J. Cardiol. 2000 • Images: van Soest et al., J. Biomed. Opt. 2010 • Images: Gambichler, et al. 2011 • Images: www.LantisLaser.com • Images: Liang, et al., 2011

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• http://www.retinalphysician.com/article.aspx?article=103653

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