Segmentation Guided Registration for 3D Spectral-Domain Optical Coherence Tomography Images

Pan, Lingjiao; Chen, Xinjian

doi:10.1109/access.2019.2943172

“…The average execution time is also evaluated. Comparison with existing methods: We compared registration performance to VOTUS+Z [11], Harris-PIIFD+Z [7], GFEMR+Z [6], GDB-ICP+Z [4], 3D-SGR [12], SRWCR [3]. The first four are well-established fundus registration methods for human eyes, but here with parameters adapted for mouse images.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Pan et al [12] and Chen et al [13] proposed layer segmentation guided 3D OCT registration and achieve good performance for human datasets. However, segmentation methods adapted from human OCT are often limited in the mouse model because of segmentation errors.…”

mentioning

confidence: 99%

Optimal Transport-based Graph Matching for 3D retinal OCT image registration

Tian¹,

Anantrasirichai²,

Nicholson³

et al. 2022

Preprint

2

0

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Registration of longitudinal optical coherence tomography (OCT) images assists disease monitoring and is essential in image fusion applications. Mouse retinal OCT images are often collected for longitudinal study of eye disease models such as uveitis, but their quality is often poor compared with human imaging. This paper presents a novel but efficient framework involving an optimal transport based graph matching (OT-GM) method for 3D mouse OCT image registration. We first perform registration of fundus-like images obtained by projecting all b-scans of a volume on a plane orthogonal to them, hereafter referred to as the x-y plane. We introduce Adaptive Weighted Vessel Graph Descriptors (AWVGD) and 3D Cube Descriptors (CD) to identify the correspondence between nodes of graphs extracted from segmented vessels within the OCT projection images. The AWVGD comprises scaling, translation and rotation, which are computationally efficient, whereas CD exploits 3D spatial and frequency domain information. The OT-GM method subsequently performs the correct alignment in the x-y plane. Finally, registration along the direction orthogonal to the x-y plane (the z-direction) is guided by the segmentation of two important anatomical features peculiar to mouse b-scans, the Internal Limiting Membrane (ILM) and the hyaloid remnant (HR). Both subjective and objective evaluation results demonstrate that our framework outperforms other well-established methods on mouse OCT images within a reasonable execution time.Retinal image registration aims to spatially align two or more retinal images captured at different times and from different viewpoints for clinical review of disease progression. Nowadays, non-invasive Optical Coherence Tomography (OCT), which enables 3-D retinal imaging, is widely used for in-vivo assessment of eye disease [1,2]. Data is generated by acquiring a series of A-scans that are combined to a 2D cross-sectional slice called a B scan. The summation of B-scans creates a fundus like projection image.An efficient longitudinal mouse OCT registration method has the potential to impact both early studies of disease mechanisms and treatment. It also helps generating strategies for analysis that are translatable to human imaging. However, imaging the mouse retina is more challenging and less well studied than is the case in humans. High-quality mouse OCT is usually expensive and hard to acquire. Low quality datasets suffer from major vascular dissimilarities,

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“…Comparison with existing methods: We compared registration performance to VOTUS+Z [11], Harris-PIIFD+Z [7], GFEMR+Z [6], GDB-ICP+Z [4], 3D-SGR [12], SRWCR [3]. The first four are well-established fundus registration methods for human eyes, but here with parameters adapted for mouse images.…”

Section: Z Direction Registrationmentioning

confidence: 99%

Optimal Transport-Based Graph Matching for 3D Retinal Oct Image Registration

Tian

¹

,

Anantrasirichai

²

,

Nicholson

³

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

3

0

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Registration of longitudinal optical coherence tomography (OCT) images assists disease monitoring and is essential in image fusion applications. Mouse retinal OCT images are often collected for longitudinal study of eye disease models such as uveitis, but their quality is often poor compared with human imaging. This paper presents a novel but efficient framework involving an optimal transport based graph matching (OT-GM) method for 3D mouse OCT image registration. We first perform registration of fundus-like images obtained by projecting all b-scans of a volume on a plane orthogonal to them, hereafter referred to as the x-y plane. We introduce Adaptive Weighted Vessel Graph Descriptors (AWVGD) and 3D Cube Descriptors (CD) to identify the correspondence between nodes of graphs extracted from segmented vessels within the OCT projection images. The AWVGD comprises scaling, translation and rotation, which are computationally efficient, whereas CD exploits 3D spatial and frequency domain information. The OT-GM method subsequently performs the correct alignment in the x-y plane. Finally, registration along the direction orthogonal to the x-y plane (the zdirection) is guided by the segmentation of two important anatomical features peculiar to mouse b-scans, the Internal Limiting Membrane (ILM) and the hyaloid remnant (HR). Both subjective and objective evaluation results demonstrate that our framework outperforms other well-established methods on mouse OCT images within a reasonable execution time.

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“…A vesselness measure is obtained on the basis of all eigenvalues of the Hessian [ 20 ]. The filter-based method is also applied in [ 21 ], but in order to obtain a high-quality projection image, the authors adopted histogram equalization followed by Wiener filtering. Filtering can also be used in conjunction with morphological operations, resulting in a precision of 83.9% [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…Effective and accurate 3D registration methods for retinal SD-OCT images are also being explored in [ 21 ] based on [ 19 ]. Such solutions require the use of two three-dimensional OCT scans, which, after appropriate labeling, allow one to obtain the x-y direction registration and the z direction registration.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction

Marciniak

¹

,

Stankiewicz

²

,

Zaradzki

³

2023

Sensors

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The use of neural networks for retinal vessel segmentation has gained significant attention in recent years. Most of the research related to the segmentation of retinal blood vessels is based on fundus images. In this study, we examine five neural network architectures to accurately segment vessels in fundus images reconstructed from 3D OCT scan data. OCT-based fundus reconstructions are of much lower quality compared to color fundus photographs due to noise and lower and disproportionate resolutions. The fundus image reconstruction process was performed based on the segmentation of the retinal layers in B-scans. Three reconstruction variants were proposed, which were then used in the process of detecting blood vessels using neural networks. We evaluated performance using a custom dataset of 24 3D OCT scans (with manual annotations performed by an ophthalmologist) using 6-fold cross-validation and demonstrated segmentation accuracy up to 98%. Our results indicate that the use of neural networks is a promising approach to segmenting the retinal vessel from a properly reconstructed fundus.

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Segmentation Guided Registration for 3D Spectral-Domain Optical Coherence Tomography Images

Cited by 7 publications

References 46 publications

Optimal Transport-based Graph Matching for 3D retinal OCT image registration

Optimal Transport-based Graph Matching for 3D retinal OCT image registration

Optimal Transport-Based Graph Matching for 3D Retinal Oct Image Registration

Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction

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