Segmentation of retinal OCT images
Dmitrij Csetverikov and József Molnár
Eötvös Loránd University, Faculty of Informatics
Abstract
3. Our method
Optical Coherence Tomography (OCT) can be efficiently used
in eye research and diagnostics, when retinal images are processed to extract borders of retinal layers. We present two novel
algorithms for delineation of three main borders in rodent retinal
images. The first, fast algorithm is based on row projections in
a sliding window. It provides initial borders for a slower but more
precise variational algorithm that iteratively refines the borders.
• Currently, segments ILM and RPE
– other internal layers in future
• Completely automatic, robust parameter setting
• Can cope with deformed and disconnected layers
• Two distinct steps, two algorithms
– fast algorithm based on row projections sets initial contours:
Comb
– then, slower but more precise variational method refines
Comb contours: Vari
1. Introduction
3.1 Comb
1.1 TÁMOP project collaborators
• Chair of Algorithms and Applications, ELTE
– Prof. Dmitrij Csetverikov, József Molnár (PhD student)
• Bascom Palmer Eye Institute, University of Miami, USA
– Dr. Delia Cabrera DeBuc, PhD students
• Department of Ophthalmology, Semmelweiss University
– Dr. Gábor Márk Somfai
• Based on row projections in a sliding window
– window height equals image height
– window width is a parameter
• Projections are smoothed and thresholded to obtain layer borders
• Robust to altered and disconnected borders
– a contour may split into several pieces
RPE1
2.408
1.864
22.58
20/20
RPE2
3.885
2.864
26.30
13/20
Table 1: Mean errors, in pixels, for the three borders. RPE1 is
the upper, RPE2 the lower border of the RPE. ‘Cmpl’ is the complete method, Comb and Vari. ‘imp %’ is the improvement due
to Vari.
– in all cases for ILM and RPE1
– in most cases for RPE2
5. Discussion and conclusion
• Current speed performance for image size 512 × 651 pixels.
– Comb: approx. 0.030 seconds per image
– Vari: 3–5 seconds per image
• Optical Coherence Tomography (OCT)
• Fast GPU implementation of Vari in progress
– in-vivo 3D imaging (non-invasive)
– fast, high-resolution
– expected speed of complete method: 2–5 images per second
• Very promising in eye research and diagnostics
• Mean deviation from ground truth: 2–3 pixels
– all seven retina layers visible
– monitoring changes, early diagnostics and prevention
– in-vivo experimenting on rodent (mice, rat) retina
– low contrast, strong speckle noise
– many layers not distinct, look discontinuous
– altered, disconnected layers in pathological cases
ILM
3.753
2.228
40.65
20/20
• Vari improves precision in almost all cases
1.2 OCT of human and rodent Retina
• Problems
border
Comb
Cmpl
imp %
imp/tot
(1)
• Ground truth questionable for the lower, less contrast RPE
border (RPE2)
(2)
Figure 2: (1) Row projections in the starting position of the window. (2) The window moving from left to right.
– occasionally, the difference between the two manual segmentations is surprisingly large
Vitreous
ILM
continous RPE borders
RPE
Choroid
Figure 1: A typical mouse retina image acquired by OCT. The
layers between ILM and RPE are hardly visible and difficult to
extract.
disconnected borders
Figure 3: Sample Comb results for continuous and disconnected RPE borders. Comb recovers from temporary loss of
data due to gaps in borders. Some contours may split into
pieces. Each piece is then processed separately by Vari.
5.1 Further work
3.2 Vari
• Ultimate goal: 3D model of retinal layers
– all layers: very difficult
– ILM and RPE: more realistic
• Studying human and rodent retina
• Uses non-parametric active contours (snakes)
– implemented via Level Sets
– works with open as well as closed curves
• Defines statistical cost function based on local intensity differences across evolving contour
• Iterations start from contours provided by Comb
– applied to each piece of contour separately
– human: bigger size, better image quality, better studied
– rodent: smaller size, worse image quality, less studied
– Fuller (2007): semi-automatic segmentation method, supervised learning
– Fabritius (2009): 2D filtering, parameters set in 3D volumes
• Slice-based (2D image) approaches
– Cabrera (2005): directional coherence-enhanced diffusion
filtering
– Szkulmowski (2007): ILM, RPE; semi-automatic, feature
thresholding
– Mishra (2009), Yazdanpanahet (2009): active contours
2.1 Problems with existing approaches
• Some methods are slow, while in practice fast processing of
large amount of data is needed
4. Test results
• Twenty images of healthy rodent retina used in tests
• Manual segmentations obtained by two independent experts
– the mean of the two manual segmentations used as the
ground truth
ILM
RPE
Figure 4: Sample retina border delineation results. Blue: Manual ground truth. Green: Comb. Red: Comb and Vari.
• Most methods are not robust in pathological cases
• Some methods need manual intervention (learning, initialisation)
– this limits speed and applicability
• No reliable ground truth is available for testing
– no generally accepted benchmark
– medical experts may disagree on location of borders
MEAN_ABS_DEVIATION
– assume layer continuity ⇒ not applicable to disconnected
layers
– have troubles with connected but altered retina
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MEAN_ABS_DEVIATION
• Volumetric (3D image) approaches
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0
0
5
10
IMAGE
ILM
15
20
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2
5
10
IMAGE
RPE1
15
• Discussion of ground truth and benchmarking
• Creating a large manually segmented database
• Testing the methods on the large database
• Numerical comparison to other techniques
• Creating 3D models of layers
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6. Publications
1. J. Molnár, D. Chetverikov, D. Cabrera DeBuc, Wei Gao, and
G.M. Somfai, ”Segmentation of rodent retinal OCT images”,
Proc. KÉPAF 2011: 8th Conference of Hungarian Association for Image Processing and Pattern Recognition, Szeged,
2011, pp.140-154.
2. J. Molnár, D. Chetverikov, D. Cabrera DeBuc, Wei Gao, and
G.M. Somfai, ”Layer extraction in rodent retinal images acquired by Optical Coherence Tomography”, Machine vision
and Applications, in print, DOI: 10.1007/s00138-011-0343-y,
2011.
Acknowledgment
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• Consulting medical experts for better understanding of problem
• Developing methods for segmentation of all layers
MEAN_ABS_DEVIATION
2. Previous work
Figure 6: Example of large difference between two experts. A
part of RPE is shown enlarged. Pink and Yellow: manual segmentations by two experts. Green: Ground truth, the mean of
the two manual borders.
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The Project is supported by the European Union and cofinanced by the European Social Fund (grant agreement no. TAMOP 4.2.1./B-09/1/KMR-2010-0003).
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IMAGE
15
20
RPE2
Figure 5: Error plots for 20 images, for the three retinal borders.
Errors are the mean absolute deviations from the ground truth,
in pixels. Continuous line: Comb. Dashed line: Comb and Vari.