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Proceedings Paper

Inter-scanner variation independent descriptors for constrained diffeomorphic demons registration of retina OCT
Author(s): S. Reaungamornrat; A. Carass; Y. He; S. Saidha; P. A. Calabresi; J. L. Prince
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Paper Abstract

Purpose: OCT offers high in-plane micrometer resolution, enabling studies of neurodegenerative and ocular-disease mechanisms via imaging of the retina at low cost. An important component to such studies is inter-scanner deformable image registration. Image quality of OCT, however, is suboptimal with poor signal-to-noise ratio and through-plane resolution. Geometry of OCT is additionally improperly defined. We developed a diffeomorphic deformable registration method incorporating constraints accommodating the improper geometry and a decentralized-modality-insensitiveneighborhood-descriptors (D-MIND) robust against degradation of OCT image quality and inter-scanner variability. Method: The method, called D-MIND Demons, estimates diffeomorphisms using D-MINDs under constraints on the direction of velocity fields in a MIND-Demons framework. Descriptiveness of D-MINDs with/without denoising was ranked against four other shape/texture-based descriptors. Performance of D-MIND Demons and its variants incorporating other descriptors was compared for cross-scanner, intra- and inter-subject deformable registration using clinical retina OCT data. Result: D-MINDs outperformed other descriptors with the difference in mutual descriptiveness between high-contrast and homogenous regions > 0.2. Among Demons variants, D-MIND-Demons was computationally efficient, demonstrating robustness against OCT image degradation (noise, speckle, intensity-non-uniformity, and poor throughplane resolution) and consistent registration accuracy [(4±4 μm) and (4±6 μm) in cross-scanner intra- and inter-subject registration] regardless of denoising. Conclusions: A promising method for cross-scanner, intra- and inter-subject OCT image registration has been developed for ophthalmological and neurological studies of retinal structures. The approach could assist image segmentation, evaluation of longitudinal disease progression, and patient population analysis, which in turn, facilitate diagnosis and patient-specific treatment.

Paper Details

Date Published: 2 March 2018
PDF: 9 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741B (2 March 2018); doi: 10.1117/12.2293790
Show Author Affiliations
S. Reaungamornrat, The Johns Hopkins Hospital (United States)
A. Carass, The Johns Hopkins Hospital (United States)
Y. He, The Johns Hopkins Hospital (United States)
S. Saidha, Johns Hopkins Univ. (United States)
P. A. Calabresi, Johns Hopkins Univ. (United States)
J. L. Prince, The Johns Hopkins Hospital (United States)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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