Segmentation guided registration of wide field-of-view retinal optical coherence tomography volumes

Subpixel motion artifacts correction and motion estimation for 3D-OCT

A number of hardware-based and software-based strategies have been suggested to eliminate motion artifacts for improvement of 3D-optical coherence tomography (OCT) image quality. However, the hardware-based strategies have to employ additional hardware to record motion compensation information. Many software-based strategies have to need additional scanning for motion correction at the expense of longer acquisition time. To address this issue, we propose a motion artifacts correction and motion estimation method for OCT volumetric imaging of anterior segment, without requirements of additional hardware and redundant scanning. The motion correction effect with subpixel accuracy for in vivo 3D-OCT has been demonstrated in experiments. Moreover, the physiological information of imaging object, including respiratory curve and respiratory rate, has been experimentally extracted using the proposed method. The proposed method offers a powerful tool for scientific research and clinical diagnosis in ophthalmology and may be further extended for other biomedical volumetric imaging applications.

Automatic segmentation of layers in chorio-retinal complex using Graph-based method for ultra-speed 1.7 MHz wide field swept source FDML optical coherence tomography

The posterior segment of the human eye complex contains two discrete microstructure and vasculature network systems, namely, the retina and choroid. We present a single segmentation framework technique for segmenting the entire layers present in the chorio-retinal complex of the human eye using optical coherence tomography (OCT) images. This automatic program is based on the graph theory method. This single program is capable of segmenting seven layers of the retina and choroid scleral interface. The graph theory was utilized to find the probability matrix and subsequent boundaries of different layers. The program was also implemented to segment angiographic maps of different chorio-retinal layers using "segmentation matrices." The method was tested and successfully validated on OCT images from six normal human eyes as well as eyes with non-exudative age-related macular degeneration (AMD). The thickness of microstructure and microvasculature for different layers located in the chorio-retinal segment of the eye was also generated and compared. A decent efficiency in terms of processing time, sensitivity, and accuracy was observed compared to the manual segmentation and other existing methods. The proposed method automatically segments whole OCT images of chorio-retinal complex with augmented probability maps generation in OCT volume dataset. We have also evaluated the segmentation results using quantitative metrics such as Dice coefficient and Hausdorff distance This method realizes a mean descent Dice similarity coefficient (DSC) value of 0.82 (range, 0.816-0.864) for RPE and CSI layer.

Five degrees-of-freedom mechanical arm with remote center of motion (RCM) device for volumetric optical coherence tomography (OCT) retinal imaging

Handheld optical coherence tomography (HH-OCT) is gaining popularity for diagnosing retinal diseases in neonates (e.g. retinopathy of prematurity). Diagnosis accuracy is degraded by hand tremor and patient motion when using commercially available handheld retinal OCT probes. This work presents a low-cost arm designed to address ergonomic challenges of holding a commercial OCT probe and alleviating hand tremor. Experiments with a phantom eye show enhanced geometric uniformity and volumetric accuracy when obtaining OCT scans with our device compared to handheld imaging approaches. An in-vivo porcine volumetric image was also obtained with the mechanical arm demonstrating clinical deployability.

Simultaneous alignment and surface regression using hybrid 2D-3D networks for 3D coherent layer segmentation of retinal OCT images with full and sparse annotations

Layer segmentation is important to quantitative analysis of retinal optical coherence tomography (OCT). Recently, deep learning based methods have been developed to automate this task and yield remarkable performance. However, due to the large spatial gap and potential mismatch between the B-scans of an OCT volume, all of them were based on 2D segmentation of individual B-scans, which may lose the continuity and diagnostic information of the retinal layers in 3D space. Besides, most of these methods required dense annotation of the OCT volumes, which is labor-intensive and expertise-demanding. This work presents a novel framework based on hybrid 2D-3D convolutional neural networks (CNNs) to obtain continuous 3D retinal layer surfaces from OCT volumes, which works well with both full and sparse annotations. The 2D features of individual B-scans are extracted by an encoder consisting of 2D convolutions. These 2D features are then used to produce the alignment displacement vectors and layer segmentation by two 3D decoders coupled via a spatial transformer module. Two losses are proposed to utilize the retinal layers' natural property of being smooth for B-scan alignment and layer segmentation, respectively, and are the key to the semi-supervised learning with sparse annotation. The entire framework is trained end-to-end. To the best of our knowledge, this is the first work that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs. Experiments on a synthetic dataset and three public clinical datasets show that our framework can effectively align the B-scans for potential motion correction, and achieves superior performance to state-of-the-art 2D deep learning methods in terms of both layer segmentation accuracy and cross-B-scan 3D continuity in both fully and semi-supervised settings, thus offering more clinical values than previous works.

Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentation

Optical coherence tomography (OCT) is the most widely used imaging modality in ophthalmology. There are multiple variations of OCT imaging capable of producing complementary information. Thus, registering these complementary volumes is desirable in order to combine their information. In this work, we propose a novel automated pipeline to register OCT images produced by different devices. This pipeline is based on two steps: a multi-modal 2D en-face registration based on deep learning, and a Z-axis (axial axis) registration based on the retinal layer segmentation. We evaluate our method using data from a Heidelberg Spectralis and an experimental PS-OCT device. The empirical results demonstrated high-quality registrations, with mean errors of approximately 46 µm for the 2D registration and 9.59 µm for the Z-axis registration. These registrations may help in multiple clinical applications such as the validation of layer segmentations among others.

Higher-order regression three-dimensional motion-compensation method for real-time optical coherence tomography volumetric imaging of the cornea

SIGNIFICANCE

Optical coherence tomography (OCT) allows high-resolution volumetric three-dimensional (3D) imaging of biological tissues in vivo. However, 3D-image acquisition can be time-consuming and often suffers from motion artifacts due to involuntary and physiological movements of the tissue, limiting the reproducibility of quantitative measurements.

AIM

To achieve real-time 3D motion compensation for corneal tissue with high accuracy.

APPROACH

We propose an OCT system for volumetric imaging of the cornea, capable of compensating both axial and lateral motion with micron-scale accuracy and millisecond-scale time consumption based on higher-order regression. Specifically, the system first scans three reference B-mode images along the C-axis before acquiring a standard C-mode image. The difference between the reference and volumetric images is compared using a surface-detection algorithm and higher-order polynomials to deduce 3D motion and remove motion-related artifacts.

RESULTS

System parameters are optimized, and performance is evaluated using both phantom and corneal (ex vivo) samples. An overall motion-artifact error of <4.61 microns and processing time of about 3.40 ms for each B-scan was achieved.

CONCLUSIONS

Higher-order regression achieved effective and real-time compensation of 3D motion artifacts during corneal imaging. The approach can be expanded to 3D imaging of other ocular tissues. Implementing such motion-compensation strategies has the potential to improve the reliability of objective and quantitative information that can be extracted from volumetric OCT measurements.

Robust three-dimensional registration on optical coherence tomography angiography for speckle reduction and visualization

BACKGROUND

In the clinical applications of optical coherence tomography angiography (OCTA), the repeated scanning and averaging method can provide better contrast with reduced speckle noises in the final results, which are useful for visualizing and quantifying vascular components with high accuracy, reproducibility, and reliability. However, the inevitable patient motion presents a challenge to this method. The objective of this study is to meet this challenge by introducing a 3D registration method to register optical coherence tomography (OCT)/OCTA scans for precise volume averaging of multiple scans to improve the signal-to-noise ratio (SNR) and increase quantification accuracy.

METHODS

The proposed method utilized both rigid affine transformation and non-rigid B-spline transformation in which their parameters were optimized and calculated by the average stochastic gradient descent on OCT structural images. In addition, we also introduced a multi-level resolution approach to further improve the robustness and computational speed of our proposed method. The imaging performance was tested on in vivo imaging of human skin and eye and assessed by SNR, peak signal-to-noise ratio (PSNR) and normalized correlation coefficient (NCC).

RESULTS

Five subjects were enrolled in this study for obtaining in vivo images of skin and retina. The proposed registration and averaging method provided substantial improvements of the imaging performance in terms of vessel connectivity and signal to noise ratio. The increase of repeated volume numbers in the averaging improves all the metrics assessed, i.e., SNR, PSNR and NCC. An improvement of the SNR from 10 to 40 dB after 10 repeated volumetric averaging was achieved.

CONCLUSIONS

The proposed 3D registration and averaging method is effective in reducing speckle noises and suppressing motion artifacts, thereby improving SNR, PSNR and NCC metrics for final averaged images. It is expected that the proposed algorithm would be practically useful in better visualization and more reliable quantification of in vivo OCT and OCTA data, which would be beneficial to OCT clinical applications.

Efficient and high accuracy 3-D OCT angiography motion correction in pathology

We describe a novel method for non-rigid 3-D motion correction of orthogonally raster-scanned optical coherence tomography angiography volumes. This is the first approach that aligns predominantly axial structural features such as retinal layers as well as transverse angiographic vascular features in a joint optimization. Combined with orthogonal scanning and favorization of kinematically more plausible displacements, subpixel alignment and micrometer-scale distortion correction is achieved in all 3 dimensions. As no specific structures are segmented, the method is by design robust to pathologic changes. Furthermore, the method is designed for highly parallel implementation and short runtime, allowing its integration into clinical workflow even for high density or wide-field scans. We evaluated the algorithm with metrics related to clinically relevant features in an extensive quantitative evaluation based on 204 volumetric scans of 17 subjects, including patients with diverse pathologies and healthy controls. Using this method, we achieve state-of-the-art axial motion correction and show significant advances in both transverse co-alignment and distortion correction, especially in the subgroup with pathology.

Numerical method for axial motion artifact correction in retinal spectral-domain optical coherence tomography

A numerical method that compensates image distortions caused by random fluctuations of the distance to an object in spectral-domain optical coherence tomography (SD OCT) has been proposed and verified experimentally. The proposed method is based on the analysis of the phase shifts between adjacent scans that are caused by micrometer-scale displacements and the subsequent compensation for the displacements through phase-frequency correction in the spectral space. The efficiency of the method is demonstrated in model experiments with harmonic and random movements of a scattering object as well as during in vivo imaging of the retina of the human eye.

Generating large field of view en-face projection images from intra-acquisition motion compensated volumetric optical coherence tomography data

A technique to generate large field of view projection maps of arbitrary optical coherence tomography (OCT) data is described. The technique is divided into two stages - an image acquisition stage that features a simple to use fast and robust retinal tracker to get motion free retinal OCT volume scans - and a stitching stage where OCT data from different retinal locations is first registered against a reference image using a custom pyramid-based approach and finally stitched together into one seamless large field of view (FOV) image. The method is applied to data recorded with a polarization sensitive OCT instrument in healthy subjects and glaucoma patients. The tracking and stitching accuracies are quantified, and finally, large FOV images of retinal nerve fiber layer retardation that contain the arcuate nerve fiber bundles from the optic nerve head to the raphe are demonstrated.

Cross-scanning optical coherence tomography angiography for eye motion correction

We propose a cross-scanning optical coherence tomography (CS-OCT) system to correct eye motion artifacts in OCT angiography images. This system employs a dual-illumination configuration with two orthogonally polarized beams, each of which simultaneously perform raster scanning in perpendicular direction with each other over the same area. In the reference arm, a polarization delay unit is used to acquire the two orthogonally polarized interferograms with a single photo detector by introducing different optical delay lines. The two cross-scanned volume data are affected by the same eye motion but in two orthogonal directions. We developed a motion correction algorithm, which removes artifacts in the slow axis of each angiogram using the other and merges them through a nonrigid registration algorithm. In this manner, we obtained a motion-corrected angiogram within a single volume scanning time without additional eye-tracking devices.

Semi-automated registration and segmentation for gingival tissue volume measurement on 3D OCT images

The change in gingival tissue volume may be used to indicate changes in gingival inflammation, which may be useful for the clinical assessment of gingival health. Properly quantifying gingival tissue volume requires a robust technique for accurate registration and segmentation of longitudinally captured 3-dimensional (3D) images. In this paper, a semi-automated registration and segmentation method for micrometer resolution measurement of gingival-tissue volume is proposed for 3D optical coherence tomography (OCT) imaging. For quantification, relative changes in gingiva tissue volume are measured based on changes in the gingiva surface height using the tooth surface as a reference. This report conducted repeatability tests on this method drawn from repeated scans in one patient, indicating an error of the point cloud registration method for oral OCT imaging is 63.08 ± 4.52µm (1σ), and the measurement error of the gingival tissue average thickness is -3.40 ± 21.85µm (1σ).

Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging

Motion artefacts from involuntary changes in eye fixation remain a major imaging issue in optical coherence tomography (OCT). This paper reviews the state-of-the-art of retrospective procedures to correct retinal motion and axial eye motion artefacts in OCT imaging. Following an overview of motion induced artefacts and correction strategies, a chronological survey of retrospective approaches since the introduction of OCT until the current days is presented. Pre-processing, registration, and validation techniques are described. The review finishes by discussing the limitations of the current techniques and the challenges to be tackled in future developments.

Spectrally encoded coherence tomography and reflectometry: Simultaneous en face and cross-sectional imaging at 2 gigapixels per second

Non-invasive biological imaging is crucial for understanding in vivo structure and function. Optical coherence tomography (OCT) and reflectance confocal microscopy are two of the most widely used optical modalities for exogenous contrast-free, high-resolution, three-dimensional imaging in non-fluorescent scattering tissues. However, sample motion remains a critical barrier to raster-scanned acquisition and reconstruction of wide-field anatomically accurate volumetric datasets. We introduce spectrally encoded coherence tomography and reflectometry (SECTR), a high-speed, multimodality system for simultaneous OCT and spectrally encoded reflectance (SER) imaging. SECTR utilizes a robust system design consisting of shared optical relays, scanning mirrors, swept laser and digitizer to achieve the fastest reported in vivo multimodal imaging rate of 2 gigapixels per second. Our optical design and acquisition scheme enable spatiotemporally co-registered acquisition of OCT cross-sections simultaneously with en face SER images for multivolumetric mosaicking. Complementary axial and lateral translation and rotation are extracted from OCT and SER data, respectively, for full volumetric estimation of sample motion with micron spatial and millisecond temporal resolution.

Spectrally encoded coherence tomography and reflectometry: Simultaneous en face and cross-sectional imaging at 2 gigapixels per second

Non-invasive biological imaging is crucial for understanding in vivo structure and function. Optical coherence tomography (OCT) and reflectance confocal microscopy are two of the most widely used optical modalities for exogenous contrast-free, high-resolution, three-dimensional imaging in non-fluorescent scattering tissues. However, sample motion remains a critical barrier to raster-scanned acquisition and reconstruction of wide-field anatomically accurate volumetric datasets. We introduce spectrally encoded coherence tomography and reflectometry (SECTR), a high-speed, multimodality system for simultaneous OCT and spectrally encoded reflectance (SER) imaging. SECTR utilizes a robust system design consisting of shared optical relays, scanning mirrors, swept laser and digitizer to achieve the fastest reported in vivo multimodal imaging rate of 2 gigapixels per second. Our optical design and acquisition scheme enable spatiotemporally co-registered acquisition of OCT cross-sections simultaneously with en face SER images for multivolumetric mosaicking. Complementary axial and lateral translation and rotation are extracted from OCT and SER data, respectively, for full volumetric estimation of sample motion with micron spatial and millisecond temporal resolution.

Characterization of Long Working Distance Optical Coherence Tomography for Imaging of Pediatric Retinal Pathology

PURPOSE

We determined the feasibility of fovea and optic nerve head imaging with a long working distance (LWD) swept source optical coherence tomography (OCT) prototype in adults, teenagers, and young children.

METHODS

A prototype swept source OCT system with a LWD (defined as distance from the last optical element of the imaging system to the eye) of 350 mm with custom fixation targets was developed to facilitate imaging of children. Imaging was performed in 49 participants from three age groups: 26 adults, 16 children 13 to 18 years old (teenagers), and seven children under 6 years old (young children) under an approved institutional review board protocol. The imaging goal was to acquire high quality scans of the fovea and optic nerve in each eye in the shortest time possible. OCT B-scans and volumes of the fovea and optic nerve head of each eligible eye were captured and graded based on four categories (lateral and axial centration, contrast, and resolution) and on ability to determine presence or absence of pathology.

RESULTS

LWD-OCT imaging was successful in 88 of 94 eligible eyes, including seven of 10 eyes of young children. Of the successfully acquired OCT images, 83% of B-scan and volumetric images, including 86% from young children, were graded as high-quality scans. Pathology was observed in high-quality OCT images.

CONCLUSIONS

The prototype LWD-OCT system achieved high quality retinal imaging of adults, teenagers, and some young children with and without pathology with reasonable alignment time.

TRANSLATIONAL RELEVANCE

The LWD-OCT system can facilitate imaging in children.