Local Quality Assessment for Optical Coherence TomographyLocal Quality Assessment for Optical Coherence TomographyP.C. Barnum1, M. Chen2, H. Ishikawa3,4, G. Wollstein3, J. Schuman3,4
1Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA (The first author performed this work at Intel Research Pittsburgh), 2Intel Research, Pittsburgh, PA, 3UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, 4Department of Bioengineering,
Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA
Expert 1 Expert 2 Expert 3 Mode AlgorithmExpert 1 - 93 94 97 93Expert 2 93 - 92 95 95Expert 3 94 92 - 97 92Mode 97 95 97 - 95Algorithm 93 95 92 95 -
Data Labeling
Classification Accuracy
Introduction to OCT
The retina is imaged in either two-dimensional slices or three-dimensional blocks, with bright colors indicating that the tissue has high reflectivity
For training and testing data, three ophthalmologists classify individual A-scans in 270 OCT images as Poor, Acceptable, or Excellent.
But even within a single image, scan quality can vary.
We aim to automatically label the quality of local regions, with a method that is insensitive to pathology.
Compress and center vertically
Classify each scale with a probabilistic SVM
Combine scales,assuming independence
Extract features for each scale
i
ibexcellentxPbbexcellentxP )|(,...),|( 21
Compress & center or histogram
Pathology and individual differences cause variation in OCT images.
To classify scan quality, we trained a hierarchy of support vector machines at multiple scales and used histogram-based metrics.
The following chart is the percent agreement between each of the three human experts, their mode, and our new algorithm. Even the human experts have difficulty distinguishing between excellent and acceptable, so all results are for discrimination between Good (either Excellent or Acceptable) and Poor.
Based on the mode of the experts, our algorithm outperforms the state of the art in local OCT quality assessment. The results for true positive to false positive are displayed separately for circumpapillary retinal nerve fiber layer, macula, and optic nerve head scans.
We use features that are robust to these effects.
RBF kernel, with probabilities estimated by fitting a sigmoid
Automated quality assessment is available for images as a whole.
High quality
Low quality
Poor Good
Nerve Fiber Layer Optic Nerve HeadMacula
ExcellentAcceptablePoor
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0.10.20.30.40.50.60.70.80.91
False Positives
True
Pos
itive
s
Stein et al.Area under the receiver operatingcharacteristics: 0.9053Hierarchical estimationArea under the receiver operatingcharacteristics: 0.9648
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0.10.20.30.40.50.60.70.80.91
False Positives
True
Pos
itive
s
Stein et al.Area under the receiver operatingcharacteristics: 0.8896Hierarchical estimationArea under the receiver operatingcharacteristics: 0.9366
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0.10.20.30.40.50.60.70.80.91
False Positives
True
Pos
itive
s
Stein et al.Area under the receiver operatingcharacteristics: 0.8890Hierarchical estimationArea under the receiver operatingcharacteristics: 0.9583
Histogram
Back of the eye
Front of the eye
Optical Coherence Tomography (OCT) is an imaging modality that allows the retina to be imaged in vivo, with micrometer precision
Quality Assessment
Mixed quality
