OCTRexpert: A Feature-Based 3D Registration Method for Retinal OCT Images

Pan, Lingjiao; Shi, Fei; Xiang, Dehui; Yu, Kai; Duan, Luwen; Zheng, Jian; Chen, Xinjian

doi:10.1109/tip.2020.2967589

Cited by 18 publications

(20 citation statements)

References 43 publications

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“…To address this issue, we updated the algorithm in such a way that it can resolve these deformations in the alignment process. To accomplish this, in the curvature curve we determined the points that are inclined relative to the vertical axis and applied the geometric transform to their coordinates before final alignment in (8). Figure 3 (a) illustrates the vertical axis (yellow) and the calculated perpendicular line (red) to the pixel of the curvature curve.…”

Section: B Shape-preserving Correctionsmentioning

confidence: 99%

“…An alignment method for preprocessing is, therefore, necessary to reduce the curvature variation across scans to improve further clinical measurements. The importance of image alignment as a preprocessing step in OCT image processing has been declared in segmentation [4]- [7], registration algorithms [8] and classification [9]- [12]. Given the importance of this preprocessing step in the accuracy of analysis and measurements, before explanation of proposed method, we review some recent OCT curvature alignment techniques.…”

Section: Introductionmentioning

confidence: 99%

“…This method was applied to normal and abnormal Bscans for segmentation of retinal layers. Pan et al [8] proposed a novel design-detection-deformation mechanism and a featurebased registration method to align OCT B-scans. They evaluated their method in registration of longitudinal OCT images from healthy subjects and subjects diagnosed with serious Choroidal Neovascularization (CNV).…”

Section: Introductionmentioning

confidence: 99%

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A Device-Independent, Shape Preserving Retinal Optical Coherence Tomography Image Alignment Method Applying TV-Unet for RPE Layer Detection

Saeedizadeh

Tajmirriahi

Haghani

et al. 2022

IEEE Trans. Instrum. Meas.

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Retinal optical coherence tomography (OCT) images are widely used in diagnosis of ocular conditions. However, random shift and orientation changes of the retinal layers in OCT B-scans yield to appearance variations across the scans. These variations reduce the accuracy of the algorithms applied in the analysis of OCT images. In this study, we propose a preprocessing step to compensate these variations and align B-scans. At first, by incorporating total variation (TV) loss in the well-known Unet model, we propose a TV-Unet model to accurately detect the retinal pigment epithelium (RPE) layer in each B-scan. Then we use the detected RPE layer in the alignment method to form a curvature curve and a reference line. A novel window transferring-based alignment approach is applied to force the curve points to form a straight line, while preserving the shape and size of the pathological lesions. Since detection of RPE layer is a crucial step in the proposed alignment method, we utilized various datasets to train and test the TV-Unet and provided a multimodal, device-independent OCT image alignment method. The TV-Unet localizes the RPE layer in OCT images with low boundary error (maximum of 1.94pixels) and high Dice coefficient (minimum of 0.98). Quantitative and qualitative results indicated that the proposed method can efficiently detects the RPE layer and align OCT images while preserving the structure and size of the retinal lesions (biomarkers) in the OCT scans.

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Section: B Shape-preserving Correctionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Device-Independent, Shape Preserving Retinal Optical Coherence Tomography Image Alignment Method Applying TV-Unet for RPE Layer Detection

Saeedizadeh

Tajmirriahi

Haghani

et al. 2022

IEEE Trans. Instrum. Meas.

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“…Deformable image registration is to establish the non-linear correspondence between a pair of images and estimate the appropriate nonlinear transformation to align a pair of images [31]. Medical professionals analyze the regions of interest (ROIs) in a unified anatomical space, which plays an important role in many clinical applications [6,12,34,40]. Traditional deformable image registration [3,32,37,45] is * corresponding author an iterative optimization process.…”

Section: Introductionmentioning

confidence: 99%

Dual-Flow Transformation Network for Deformable Image Registration with Region Consistency Constraint

Ma¹,

Yibo²,

Xia³

et al. 2021

Preprint

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Deformable image registration is able to achieve fast and accurate alignment between a pair of images and thus plays an important role in many medical image studies. The current deep learning (DL)-based image registration approaches directly learn the spatial transformation from one image to another by leveraging a convolutional neural network, requiring ground truth or similarity metric. Nevertheless, these methods only use a global similarity energy function to evaluate the similarity of a pair of images, which ignores the similarity of regions of interest (ROIs) within images. Moreover, DL-based methods often estimate global spatial transformations of image directly, which never pays attention to region spatial transformations of ROIs within images. In this paper, we present a novel dual-flow transformation network with region consistency constraint which maximizes the similarity of ROIs within a pair of images and estimates both global and region spatial transformations simultaneously. Experiments on four public 3D MRI datasets show that the proposed method achieves the best registration performance in accuracy and generalization compared with other state-of-the-art methods.

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“…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

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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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OCTRexpert: A Feature-Based 3D Registration Method for Retinal OCT Images

Cited by 18 publications

References 43 publications

A Device-Independent, Shape Preserving Retinal Optical Coherence Tomography Image Alignment Method Applying TV-Unet for RPE Layer Detection

A Device-Independent, Shape Preserving Retinal Optical Coherence Tomography Image Alignment Method Applying TV-Unet for RPE Layer Detection

Dual-Flow Transformation Network for Deformable Image Registration with Region Consistency Constraint

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

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