Longitudinal graph-based segmentation of macular OCT using fundus alignment

Segmentation of retinal layers in optical coherence tomography (OCT) is an essential step in OCT image analysis for screening, diagnosis, and assessment of retinal disease progression. Real-time segmentation together with high-speed OCT volume acquisition allows rendering of en face OCT of arbitrary retinal layers, which can be used to increase the yield rate of high-quality scans, provide real-time feedback during image-guided surgeries, and compensate aberrations in adaptive optics (AO) OCT without using wavefront sensors. We demonstrate here unprecedented real-time OCT segmentation of eight retinal layer boundaries achieved by 3 levels of optimization: 1) a modified, low complexity, neural network structure, 2) an innovative scheme of neural network compression with TensorRT, and 3) specialized GPU hardware to accelerate computation. Inferencing with the compressed network U-NetRT took 3.5 ms, improving by 21 times the speed of conventional U-Net inference without reducing the accuracy. The latency of the entire pipeline from data acquisition to inferencing was only 41 ms, enabled by parallelized batch processing. The system and method allow real-time updating of en face OCT and OCTA visualizations of arbitrary retinal layers and plexuses in continuous mode scanning. To the best our knowledge, our work is the first demonstration of an ophthalmic imager with embedded artificial intelligence (AI) providing real-time feedback.

At the present time optical coherence tomography (OCT) is among the most commonly used non-invasive imaging methods for the acquisition of large volumetric scans of human retinal tissues and vasculature. The substantial increase of accessible highly resolved 3D samples at the optic nerve head and the macula is directly linked to medical advancements in early detection of eye diseases. To resolve decisive information from extracted OCT volumes and to make it applicable for further diagnostic analysis, the exact measurement of retinal layer thicknesses serves as an essential task be done for each patient separately. However, manual examination of OCT scans is a demanding and time consuming task, which is typically made difficult by the presence of tissue-dependent speckle noise. Therefore, the elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven geometric approach to order-constrained 3D OCT retinal cell layer segmentation which takes as input data in any metric space and can be implemented using only simple, highly parallelizable operations. As opposed to many established retinal layer segmentation methods, we use only locally extracted features as input and do not employ any global shape prior. The physiological order of retinal cell layers and membranes is achieved through the introduction of a smoothed energy term. This is combined with additional regularization of local smoothness to yield highly accurate 3D segmentations. The approach thereby systematically avoid bias pertaining to global shape and is hence suited for the detection of anatomical changes of retinal tissue structure. To demonstrate its robustness, we compare two different choices of features on a data set of manually annotated 3D OCT volumes of healthy human retina. The quality of computed segmentations is compared to the state of the art in automatic retinal layer segmention as well as to manually annotated ground truth data in terms of mean absolute error and Dice similarity coefficient. Visualizations of segmented volumes are also provided.

Retinal layer thickness is an important bio-marker for people with multiple sclerosis (PwMS). In clinical practice, retinal layer thickness changes in optical coherence tomography (OCT) are widely used for monitoring multiple sclerosis (MS) progression. Recent developments in automated retinal layer segmentation algorithms allow cohort-level retina thinning to be observed in a large study of PwMS. However, variability in these results make it difficult to identify patient-level trends; this prevents patient specific disease monitoring and treatment planning using OCT. Deep learning based retinal layer segmentation algorithms have achieved state-of-the-art accuracy, but the segmentation is performed on each individual scan without utilizing longitudinal information, which can be important in reducing segmentation error and reveal subtle changes in retinal layers. In this paper, we propose a longitudinal OCT segmentation network which achieves more accurate and consistent layer thickness measurements for PwMS.

7 - Optical coherence tomography: A review

Diabetic mellitus causes microvasculature changes in the retina which leads to diabetic retinopathy and may cause blindness if left unchecked. Spectral-Domain Optical Coherence Tomography (SDOCT) is a noninvasive imaging modality which could give precise information about the retinal layers. SDOCT retinal images of 75 subjects with uncontrolled diabetic mellitus for more than 2 years duration and images of 30 subjects with controlled diabetes or in normal condition are considered. The speckle noise in the images is smoothened using anisotropic diffusion filtering technique, and segmentation of Retinal Nerve Fiber layer (RNFL) along with Ganglion Cell Layer (GCL) and Inner Plexiform Layer (IPL) complex is performed using the axial gradient canny edge detection combined with a level set method. Textural features are obtained from the segmented layers, and classification of abnormality is done using SVM. The results showed that the retinal nerve fiber layer along with GCL+IPL complex thickness was reduced in subjects with even minimal diabetic retinopathy.

This work investigates the ability of texture analysis to yield discrimination of retinal tissue layers in the images provided by Optical Coherence Tomography (OCT). In fact, this relatively new imaging technology allows noninvasive visualization of retinal layers. Their segmentation is a prerequisite for any computer method that aims to objectively extract valuable information, regarding the condition and the progression of disease and therapy. Since the regularities of biological tissue can be captured by texture analysis in a straightforward way, a computer approach is proposed based on co-occurence matrices and artificial neural networks (ANN) for the classification and analysis of single retinal layers. A subset of ten normal eyes has been used for the training phase, and another subset of ten normal eyes has been used for testing the system performance. For inner retinal layers, accuracy was 79%, specificity about 71% and sensibility was 87%. Slightly lower values were obtained for outer retinal layers. These preliminary results suggest that this approach may be useful as a prototype system for a quantitative characterization of retinal tissue.

Automated, anatomically coherent retinal layer segmentation in optical coherence tomography (OCT) is one of the most important components of retinal disease management. However, current methods rely on large amounts of labeled data, which can be difficult and expensive to obtain. In addition, these systems tend often propose anatomically impossible results, which undermines their clinical reliability. This study introduces a semi-supervised approach to retinal layer segmentation that leverages large amounts of unlabeled data and anatomical prior knowledge related to the structure of the retina. During training, we use a novel topological engine that converts inferred retinal layer boundaries into pixel-wise structured segmentations. These compose a set of anatomically valid disentangled representations which, together with predicted style factors, are used to reconstruct the input image. At training time, the retinal layer boundaries and pixel-wise predictions are both guided by reference annotations, where available, but more importantly by innovatively exploiting anatomical priors that improve the performance, robustness and coherence of the method even if only a small amount of labeled data is available. Exhaustive experiments with respect to label efficiency, contribution of unsupervised data and robustness to different acquisition settings were conducted. The proposed method showed state of-the-art performance on all the studied public and internal datasets, specially in low annotated data regimes. Additionally, the model was able to make use of unlabeled data from a different domain with only a small performance drop in comparison to a fully-supervised setting. A novel, robust, label-efficient retinal layer segmentation method was proposed. The approach has shown state-of-the-art layer segmentation performance with a fraction of the training data available, while at the same time, its robustness against domain shift was also shown.

As a function of the spatial position of the optical coherence tomography (OCT) image, retinal layer thickness is an important diagnostic indicator for many retinal diseases. Reliable segmentation of the retinal layer is necessary for extracting useful clinical information. However, manual segmentation of these layers is time-consuming and prone to bias. Furthermore, due to speckle noise, low image contrast, retinal detachment, and also irregular morphological features make the automatic segmentation task challenging. To alleviate these challenges, in this paper, we propose a new coarse-fine framework combining the full convolutional network (FCN) with a multiphase level set (named FCN-MLS) for automatic segmentation of nine boundaries in retinal spectral OCT images. In the coarse stage, FCN is used to learn the characteristics of specific retinal layer boundaries and achieve classification of four retinal layers. The boundaries are then extracted and the remaining boundaries are initialized based on a priori information about the thickness of the retinal layer. In order to prevent the overlapping of the segmentation interfaces, a regional restriction technique is used in the multi-phase level to evolve the boundaries to achieve fine nine retinal layers segmentation. Experimental results on 1280 B-scans show that the proposed method can segment nine retinal boundaries accurately. Compared with the manual delineation, the overall mean absolute boundary location difference and the overall mean absolute thickness difference were 5.88 ± 2.38μm and 5.81 ± 2.19μm, which showed a good consistency with manual segmentation by the physicians. Our experimental results also outperform state-of-the-art methods.

Optical coherence tomography (OCT) is a new imaging technology which is widely used in the field of ophthalmology, and retinal tissue layers segmentation plays an important role in the diagnosis of retinal diseases. This paper proposed an OCT macular retinal segmentation method based on the prior information of retinal structure and the optimized boundary tracking algorithm and realized the automatic segmentation of nine retinal layers. After image preprocessing, according to the multi-scale morphological operations and retinal structure characteristics, the optimal initial points were acquired in the parafovea domain. According to the new definition of boundary description feature, this paper optimized the traditional boundary tracking algorithm, and segmented the retinal boundaries. This paper analyzed 100 retinal OCT images, which come from 50 healthy participants from 18 to 29 years old, then compared our segmentation results with graph-based segmentation results and manual segmentations labeled by two experts. Experimental results showed that our method can accurately and effectively segment nine retinal layers (mean square error of boundary position is 1.18 ± 0.40 pixels), and is close to the results of manual segmentation (1.06±0.22 pixels), better than the literature segmentation results (3.02±1.03 pixels).

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer’s, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity.

Quantification of retinal layers using automated segmentation of optical coherence tomography (OCT) images allows for longitudinal studies of retinal and neurological disorders in mice. The purpose of this study was to compare the performance of automated retinal layer segmentation algorithms with data from manual segmentation in mice using the Spectralis OCT. Spectral domain OCT images from 55 mice from three different mouse strains were analyzed in total. The OCT scans from 22 C57Bl/6, 22 BALBc, and 11 C3A.Cg-Pde6b+Prph2Rd2 /J mice were automatically segmented using three commercially available automated retinal segmentation algorithms and compared to manual segmentation. Fully automated segmentation performed well in mice and showed coefficients of variation (CV) of below 5% for the total retinal volume. However, all three automated segmentation algorithms yielded much thicker total retinal thickness values compared to manual segmentation data (P < 0.0001) due to segmentation errors in the basement membrane. Whereas the automated retinal segmentation algorithms performed well for the inner layers, the retinal pigmentation epithelium (RPE) was delineated within the sclera, leading to consistently thicker measurements of the photoreceptor layer and the total retina. The introduction of spectral domain OCT allows for accurate imaging of the mouse retina. Exact quantification of retinal layer thicknesses in mice is important to study layers of interest under various pathological conditions.

Background:The present study aims to evaluate the effect of photodynamic therapy (PDT) on the thickness of segmentation layers of the retina in cases with central serous chorioretinopathy (CSCR).Materials and Methods:This was a prospective, observational study on cases with CSCR who were candidates for PDT therapy. All patients had undergone at least 1 month of conservative management without satisfactory resolution. PDT was carried out according to the safe half-dose therapy scheme. Spectral-domain optical coherence tomography was employed to evaluate the changes in morphology and segmentation of retinal layers. Patients were followed up for 3 months.Results:Twenty-seven cases (18 males and 9 females) were included. Age of the patients varied from 39 to 59 years (mean: 46.61 ± 12.48 years). Cases were followed for 92.17 ± 3.28 days. Sixteen cases had functional and anatomical improvement by the treatment. Changes in overall retinal (377.39 ± 61.36 to 323.61 ± 71.36; P = 0.004) and all outer retinal segmentation layers including outer plexiform layer (34.93 ± 10.07 to 29.25 ± 6.12; P = 0.008), outer nuclear layer (63.52 ± 30.44 to 46.44 ± 20.62; P = 0.017), and retinal pigment epithelium (40.66 ± 37.73 to 23.78 ± 29.33; P = 0.016) were statistically significant. On the contrary, inner retinal segmentation layers, especially retinal ganglion cell (RGC) layer (38.29 ± 16.63 to 37.26 ± 16.18; P = 0.387), remained statistically unchanged.Conclusion:We postulate that PDT alleviates outer retinal edema where fluid accumulation occurs mostly, whereas it does not alter inner retinal and especially RGC layer. These findings may indicate that short-term atrophy of the inner retina did not occur following PDT and may point toward safety of this method for cases with CSCR.

This study aimed to analyze longitudinal changes in retinal microstructures following acute optic neuritis and to identify the factors that affect those changes using spectral-domain optical coherence tomography (OCT). Forty-eight eyes of 37 patients with a first episode of optic neuritis and 48 eyes of 48 healthy controls were enrolled. Patients underwent serial OCT and visual function testing for more than six months. Individual layers from macular OCT were segmented with an automated algorithm. The total retinal layer (TRL), nerve fiber layer (NFL), ganglion cell layer (GCL) and inner plexiform layer (IPL) of optic neuritis eyes showed significant thinning with time over 6–15 months (p < 0.001 for all). The outer nuclear layer (ONL) showed a later decrease in thickness (p = 0.007). The outer retinal layer (ORL) showed an increase (p = 0.007) in thickness at two to five months which was sustained over time. Low visual acuity and neuromyelitis optica (NMO) immunoglobulin (Ig) G were associated with changes in the thickness of the GCL, IPL, and ONL over time (p < 0.05 for all). Low visual acuity was also associated with changes in the thickness of the NFL over time (p = 0.033). Dynamic changes of retinal microstructures varied according to the retinal layer examined after an optic neuritis attack. Initial visual acuity and NMO-IgG were found to be significant factors affecting the changes in thickness of each retinal layer. These results will lead to a better understanding of the pathologic changes that occur in eyes with optic neuritis.

Retinal layer thickness, evaluated as a function of spatial position from optical coherence tomography (OCT) images is an important diagnostics marker for many retinal diseases. However, due to factors such as speckle noise, low image contrast, irregularly shaped morphological features such as retinal detachments, macular holes, and drusen, accurate segmentation of individual retinal layers is difficult. To address this issue, a computer method for retinal layer segmentation from OCT images is presented. An efficient two-step kernel-based optimization scheme is employed to first identify the approximate locations of the individual layers, which are then refined to obtain accurate segmentation results for the individual layers. The performance of the algorithm was tested on a set of retinal images acquired in-vivo from healthy and diseased rodent models with a high speed, high resolution OCT system. Experimental results show that the proposed approach provides accurate segmentation for OCT images affected by speckle noise, even in sub-optimal conditions of low image contrast and presence of irregularly shaped structural features in the OCT images.

This study investigated the retinal segmental thicknesses in individuals with pseudoexfoliation syndrome and ocular hypertension. Maximum thinning was found at 6 mm inferior to the inner plexiform layer. This layer is very important for the early diagnosis of glaucoma. To analyze the thickness of the peripapillary retinal nerve fiber layer and 8 macular layers using optical coherence tomography in eyes with ocular hypertension (OHT) and pseudoexfoliation syndrome (PXS) and healthy eyes and to evaluate between-group differences. In a prospective study, the macular segmentation of retinal layers in 120 eyes of 120 participants was performed automatically using current Heidelberg Spectralis optical coherence tomography software, which provides measurements for 8 retinal layers. Thickness maps divided into nine subfields (ie, 1, 3, and 6mm) were extracted from the software for each retinal layer and compared between groups. The thinnest macular layers appeared in the ocular hypertensive PXS, normotensive PXS, and OHT groups in that order. In the inner retinal layers (macular retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer), statistically significant differences emerged between the PXS and control groups in all quadrants of the 3 and 6mm areas. No significant difference between the OHT group and control group appeared except in the 6mm temporal quadrant of the inner plexiform layer (IPL). Receiving operating characteristic analysis revealed quadrants with high area-under-the-curve values at 3 and 6mm in macular segments in all 3 groups compared with the control group. In macular segment analysis, the inner retinal layers showed the most thinning in patients with ocular hypertensive PXS. According to receiving operating characteristic curve analysis, examinations performed 6mm inferior to the IPL, as the quadrant with the highest area under the curve in all 3 groups, are critical for the early diagnosis of glaucoma.