poster - Ophthalmic Image Analysis (OPTIMA)
Automatic segmentation of the posterior vitreous boundary in retinal optical coherence tomography Alessio Montuoro1, Sebastian M. Waldstein1, Ana-Maria Glodan1, Dominika Podkowinski1, 1 2 1 1 Bianca S. Gerendas , Georg Langs , Christian Simader , Ursula Schmidt-Erfurth 5275 Christian Doppler Laboratory for Ophthalmic Image Analysis (OPTIMA) 1 Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Austria 2 Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria Financial disclosures: None Methodology Introduction Disorders of the vitreomacular interface such as vitreomacular traction and macular hole formation have recently been made accessible to pharmacologic treatment by the introduction of enzymatic vitreolysis. However, this therapeutic option is only efficacious in a subset of patients with strictly defined patterns of vitreous adhesions. Moreover, posterior vitreous detachment has been demonstrated to impact the efficacy of intravitreally administered antiangiogenic agents. Therefore, precise and reproducible detection, quantification and classification of the posterior vitreous boundary and its adhesions at the macula is of major importance. The aim of this study was to develop a method to fully automatically segment the vitreous boundary in Spectral Domain - Optical Coherence Tomography (SD-OCT) scans. Results Estimate local image orientation Raw Image Moment π₯π πππ = π₯ β π¦π β πΌ[π₯, π¦] π10 π₯= π00 Central Image Moment π πππ = β² πππ = π01 π¦= π00 πππ Image Orientation β² 2 β π11 π πΌ = arctan β² β² π20 β π02 Covariance Matrix β² π20 πππ£ πΌ = β² π11 π (π₯ β π₯) β (π¦ β π¦) β πΌ[π₯, π¦] π₯ π¦ Centroid Bottom: iterative segmentation refinement and final segmentation after 5 iterations 2 The orientation is given by the angle of the eigenvector with the largest eigenvalue π¦ β² π11 β² π02 The eigenvectors of this covariance matrix correspond to major/minor axes π00 Masked Local Image Moments π₯ π β π¦ π β πΌπ€ π₯, π¦ β πππ π[π₯, π¦] πππ = π₯ π¦ (π₯ β π₯)π β π¦ β π¦ πππ = π₯ π β πΌπ€ π₯, π¦ β πππ π[π₯, π¦] π¦ Extract patches and compute eigenfeatures (a) Data A set of 88 macula-centered Heidelberg Spectralis SD-OCT volume scans from patients available at the Vienna Reading Center was included. The posterior vitreous boundary was manually annotated in 337 B-scans and the automatic Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE) segmentation was extracted. 1. Extract 21 x 21 patches around each pixel in training set 2. Rotate according local image orientation 3. Compute Principle Component Analysis 4. Use resulting eigenvectors as filters for feature generation Train Random Forest classifier and predict Top: Map of the distance between the posterior vitreous boundary and the inner limiting membrane Left: 3D visualization of the posterior vitreous boundary (gray), ILM (green) and RPE (red) Ground truth Preliminary segmentation Comparison with expert annotation Vitreous Vitreous cortex Vitreous Vitreous cortex Between vitreous cortex and ILM excluded Between ILM and RPE Between ILM and RPE Below RPE The pixel distance between the automatic ILM segmentation and the manual posterior vitreous boundary annotation was used as ground truth. This was compared to the pixel distance between the ILM and the automatic segmentation result. The test set consists of 11 randomly chosen SD-OCT volumes. Between vitreous cortex and ILM We use a 3D graph cut approach to find a preliminary segmentation. The poor results are due to the fact that classes have similar appearance (and therefore similar feature representation). Therefore additional features are needed β distance from VMI, ILM and RPE β local spatial context Ground truth Segmentation after 1 iteration Train Random Forest classifier with additional features Below RPE Vitreous Vitreous cortex This annotations were used to assign each voxel in the SD-OCT volume to one of 5 classes: By repeating this step an iterative refinement of the segmentation results can be achieved. β’ Vitreous β’ Vitreous cortex β’ β’ Between vitreous cortex and ILM β’ Volume between vitreous cortex and ILM The voxel that was manually annotated β’ Between the ILM and the RPE and the 3 voxels above 20 voxels above that where excluded from β’ Below the RPE the training set Between ILM and RPE Below RPE Department of Ophthalmology | http://optima.meduniwien.ac.at Financially supported by the Austrian Federal Ministry of Science, Research and Economy and the National Foundation for Research, Technology and Development. Top: logarithmic plot of manual vs. automatic segmentation Far left: segmentation accuracy increase after context iterations Left: segmentation error histogram after 5 iterations Conclusion & Future Work We have presented a method for the automatic segmentation of the posterior vitreous boundary in retinal optical coherence using rotation invariant eigenfeatures. By using an iterative refinement the spatial context of classes could be automatically learned from training data. A similar approach has been used for retinal vessel segmentation in color fundus images (see [1]) showing that this approach can be applied to a variety of segmentation tasks. The current system is limited to SD-OCT scans acquired with the Heidelberg Spectralis scanner, furthermore the ILM and RPE segmentation used for training is computed automatically and is not guaranteed to be correct. Manual annotations of scans of different vendors and of the ILM and RPE surfaces are currently performed which should overcome this limitations. [1] Rotation invariant eigenvessels and auto-context for retinal vessel detection Alessio Montuoro; Christian Simader; Georg Langs; Ursula Schmidt-Erfurth; Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94131F (March 20, 2015); doi:10.1117/12.2081918. [email protected]
© Copyright 2024