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

7/30/2011

1

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

7/30/2011

2

OPTICAL COHERENCE TOMOGRAPHY

5mm

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

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

The 6th Imaging Modality

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

7/30/2011

3

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.

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

7/30/2011

4

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

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

7/30/2011

5

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

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

7/30/2011

6

OPHTHALMOLOGY AND OPTICAL COHERENCE TOMOGRAPHY

Eye Anatomy 101

7/30/2011

7

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. Thats ~10 million people. It can lead to blindness.

http://telemedicine.orbis.org/bins/home.asphttp://www.allaboutvision.com/http://www.diabetes.org/diabetes-basics/diabetes-statistics/http://www.diabetes.org/diabetes-basics/diabetes-statistics/http://www.diabetes.org/diabetes-basics/diabetes-statistics/http://www.diabetes.org/diabetes-basics/diabetes-statistics/http://www.diabetes.org/diabetes-basics/diabetes-statistics/

7/30/2011

8

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

7/30/2011

9

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

7/30/2011

10

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.

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

7/30/2011

11

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

http://www.ophmanagement.com/

7/30/2011

12

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), 95105 (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, 12616 (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

7/30/2011

13

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

7/30/2011

14

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.

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

7/30/2011

15

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 interests shape/appearance Constrains optimization space

Fills in noisy data

More Recent Developments in Image Processing

7/30/2011

16

GRAPH-BASED 1d Graph Traversal

Images cast as Graphs

Graph G=(V,E)

7/30/2011

17

Dijkstras 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

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

7/30/2011

18

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

7/30/2011

19

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

+ 1, =

7/30/2011

20

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

Its 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 Dijkstras 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

7/30/2011

21

Dijkstras 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

7/30/2011

22

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.

7/30/2011

23

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)

7/30/2011

24

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)

7/30/2011

25

Pros: Efficient global optimization tools

Current state of art

Cons: Only suitable for 1d structures

Cant handle bubbles or object boundaries in volumetric data

Dynamic Programming Shortest Paths

GRAPH-BASED N-d Graph-Cuts

7/30/2011

26

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)( spp IIsD

22 2/||||exp)( tpp IItD

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

Graph-cuts Boykov-Jolly 2001

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

7/30/2011

27

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:

7/30/2011

28

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

7/30/2011

29

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

Kaji 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)

7/30/2011

30

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 Bruchs membrane

appears to extend past the scleral margin

4. Bruchs Endpoint

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

7/30/2011

31

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)

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

7/30/2011

32

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

7/30/2011

33

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

7/30/2011

34

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.

7/30/2011

35

COMMERCIAL ADOPTION Intra-retinal Segmentation Algorithms

Spectral/Fourier Domain OCT

http://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdf http://www.revoptom.com/content/i/796/c/14792/

http://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.revoptom.com/content/i/796/c/14792/http://www.revoptom.com/content/i/796/c/14792/

7/30/2011

36

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

Clinical use requires

7/30/2011

37

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

7/30/2011

38

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

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

7/30/2011

39

Cup & Disc Segmentation

Courtesy of Robert Chang MD, Stanford University

Followed by Carl Zeiss Meditec Inc.

REGISTRATION Is change pathological?

7/30/2011

40

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) 209219 http://www.tecn.upf.es/~afrangi/articles/miccai1998.pdf

Create 2d image

Limit integration

range

Generate and register vessel maps

7/30/2011

41

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

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

7/30/2011

42

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

7/30/2011

43

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

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

7/30/2011

44

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

7/30/2011

45

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 (

7/30/2011

46

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

7/30/2011

47

FUTURE TRENDS

Steady evolution of the hardware

Faster cameras

Different wavelengths

Combo modalities

Lower costs, etc.

Cheaper components

Hardware

7/30/2011

48

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

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

7/30/2011

49

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

jonathan@voxeleron.com

daniel@voxeleron.com

Thank you!

mailto:jonathan@voxeleron.commailto:daniel@voxeleron.com

7/30/2011

50

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), 95105 (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, 12616 (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

100

http://obel.ee.uwa.edu.au/research/oct/intro/http://obel.ee.uwa.edu.au/research/oct/intro/http://www.octnews.org/http://www.recendt.at/517_ENG_HTML.phphttp://www.coollab.net/fileadmin/coollab_upload/coollab/docs/huang-new_OCT_ophthalmology.pdfhttp://www.coollab.net/fileadmin/coollab_upload/coollab/docs/huang-new_OCT_ophthalmology.pdfhttp://www.coollab.net/fileadmin/coollab_upload/coollab/docs/huang-new_OCT_ophthalmology.pdfhttp://telemedicine.orbis.org/bins/home.asphttp://www.allaboutvision.com/http://www.diabetes.org/diabetes-basics/diabetes-statistics/http://www.diabetes.org/diabetes-basics/diabetes-statistics/http://www.diabetes.org/diabetes-basics/diabetes-statistics/http://www.diabetes.org/diabetes-basics/diabetes-statistics/http://www.diabetes.org/diabetes-basics/diabetes-statistics/http://www.meditec.zeiss.com/C125679E00525939/ContainerTitel/CirrusOCT/$File/retinal-measurements.pdfhttp://www.meditec.zeiss.com/C125679E00525939/ContainerTitel/CirrusOCT/$File/retinal-measurements.pdfhttp://www.meditec.zeiss.com/C125679E00525939/ContainerTitel/CirrusOCT/$File/retinal-measurements.pdfhttp://www.meditec.zeiss.com/C125679E00525939/ContainerTitel/CirrusOCT/$File/retinal-measurements.pdfhttp://www.meditec.zeiss.com/C125679E00525939/ContainerTitel/CirrusOCT/$File/retinal-measurements.pdfhttp://www.ophmanagement.com/

7/30/2011

51

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/

101

References

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

Kaji 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

102

http://www.mathworks.com/matlabcentral/fileexchange/14862-sticks-filter/content/sf.mhttp://www.mathworks.com/matlabcentral/fileexchange/14862-sticks-filter/content/sf.mhttp://www.mathworks.com/matlabcentral/fileexchange/14862-sticks-filter/content/sf.mhttp://www.mathworks.com/matlabcentral/fileexchange/14862-sticks-filter/content/sf.mhttp://www.mathworks.com/matlabcentral/fileexchange/14862-sticks-filter/content/sf.mhttp://www.algolist.com/Dijkstra's_algorithmhttp://www.algolist.com/Dijkstra's_algorithmhttp://www.csd.uoc.gr/~komod/ICCV07_tutorial/http://www.csd.uoc.gr/~komod/ICCV07_tutorial/http://www.gig.pitt.edu/Research3.htmlhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdfhttp://www.wiley-vch.de/berlin/journals/op/09-04/OP0904_S24-S28.pdf

7/30/2011

52

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) 209219

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

103

References

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

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

104

http://www.revoptom.com/content/i/796/c/14792/http://www.revoptom.com/content/i/796/c/14792/http://www.ophthalmologyweb.com/FeaturedArticle.aspx?spid=23&aid=337http://www.oct-optovue.com/images/rnfl.jpghttp://www.oct-optovue.com/images/rnfl.jpghttp://www.oct-optovue.com/images/rnfl.jpghttp://www.tecn.upf.es/~afrangi/articles/miccai1998.pdfhttp://www.tecn.upf.es/~afrangi/articles/miccai1998.pdfhttp://www.visioncareprofessional.com/ereader/zeiss/1/czm.pdfhttp://www.springerlink.com/content/386552t20738t268/http://www.meditec.zeiss.com/88256DE3007B916B/0/D023F17419FBEBFDC12576E4003985F6/$file/cirrus_4-0_en.pdfhttp://www.meditec.zeiss.com/88256DE3007B916B/0/D023F17419FBEBFDC12576E4003985F6/$file/cirrus_4-0_en.pdfhttp://www.meditec.zeiss.com/88256DE3007B916B/0/D023F17419FBEBFDC12576E4003985F6/$file/cirrus_4-0_en.pdfhttp://www.meditec.zeiss.com/88256DE3007B916B/0/D023F17419FBEBFDC12576E4003985F6/$file/cirrus_4-0_en.pdfhttp://www.retinalphysician.com/article.aspx?article=103653