Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images

Comprehensive automatic processing and analysis of adaptive optics flood illumination retinal images on healthy subjects

This work presents a novel fully automated method for retinal analysis in images acquired with a flood illuminated adaptive optics retinal camera (AO-FIO). The proposed processing pipeline consists of several steps: First, we register single AO-FIO images in a montage image capturing a larger retinal area. The registration is performed by combination of phase correlation and the scale-invariant feature transform method. A set of 200 AO-FIO images from 10 healthy subjects (10 images from left eye and 10 images from right eye) is processed into 20 montage images and mutually aligned according to the automatically detected fovea center. As a second step, the photoreceptors in the montage images are detected using a method based on regional maxima localization, where the detector parameters were determined with Bayesian optimization according to manually labeled photoreceptors by three evaluators. The detection assessment, based on Dice coefficient, ranges from 0.72 to 0.8. In the next step, the corresponding density maps are generated for each of the montage images. As a final step, representative averaged photoreceptor density maps are created for the left and right eye and thus enabling comprehensive analysis across the montage images and a straightforward comparison with available histological data and other published studies. Our proposed method and software thus enable us to generate AO-based photoreceptor density maps for all measured locations fully automatically, and thus it is suitable for large studies, as those are in pressing need for automated approaches. In addition, the application MATADOR (MATlab ADaptive Optics Retinal Image Analysis) that implements the described pipeline and the dataset with photoreceptor labels are made publicly available.

CT image crack segmentation method based on linear feature enhancement

Industrial computed tomography (CT) crack segmentation is a key technology in industrial CT image processing. Unfortunately, the interference of artifact and noise in CT image often bring great trouble to the crack segmentation. In order to improve the segmentation accuracy of cracks in CT images, we propose to develop and test a new crack segmentation algorithm based on linear feature enhancement by analyzing the features of cracks in CT images. Firstly, the total variational model is used to denoise the input image. Next, a Frangi multiscale filter is used to extract linear structures in the image, and then the extracted linear structures are used to enhance the contrast of the image. Finally, the cracks in the image are detected and segmented by Otsu algorithm. By comparing with the manual segmentation results, the average intersection-over-union (IOU) reaches 86.10% and the average F1 score reaches 92.44%, which verifies the effectiveness and correctness of the algorithm developed in this study. Overall, experiments demonstrate that the new algorithm improves the accuracy of crack segmentation and it is effective applying to industry CT images.

Comparison of confocal and non-confocal split-detection cone photoreceptor imaging

Quadrant reflectance confocal and non-confocal scanning light ophthalmoscope images of the photoreceptor mosaic were recorded in a subject with congenital achromatopsia (ACHM) and a normal control. These images, captured with various circular and annular apertures, were used to calculate split-detection images, revealing two cone photoreceptor contrast mechanisms. The first contrast mechanism, maximal in the non-confocal 5.5-10 Airy disk diameter annular region, is unrelated to the cone reflectivity in confocal or flood illumination imaging. The second mechanism, maximal for confocal split-detection, is related to the cone reflectivity in confocal or flood illumination imaging that originates from the ellipsoid zone and/or inner-outer segment junction. Seeking to maximize image contrast, split-detection images were generated using various quadrant detector combinations, with opposite (diagonal) quadrant detectors producing the highest contrast. Split-detection generated with the addition of adjacent quadrant detector pairs, shows lower contrast, while azimuthal split-detection images, calculated from adjacent quadrant detectors, showed the lowest contrast. Finally, the integration of image pairs with orthogonal split directions was used to produce images in which the photoreceptor contrast does not change with direction.

Optical Incoherence Tomography: a method to generate tomographic retinal cross-sections with non-interferometric adaptive optics ophthalmoscopes

We present Optical Incoherence Tomography (OIT): a completely digital method to generate tomographic retinal cross-sections from en-face through-focus image stacks acquired by non-interferometric imaging systems, such as en-face adaptive optics (AO)-ophthalmoscopes. We demonstrate that OIT can be applied to different imaging modalities using back-scattered light, including systems without inherent optical sectioning and, for the first time, multiply-scattered light, revealing a distinctive cross-sectional view of the retina. The axial dimension of OIT cross-sections is given in terms of focus position rather than optical path, as in OCT. We explore this property to guide focus position in cases where the user is "blind" focusing, allowing precise plane selection for en-face imaging of retinal pigment epithelium, the vascular plexuses and translucent retinal neurons, such as photoreceptor inner segments and retinal ganglion cells, using respectively autofluorescence, motion contrast and split detection techniques.

ATTENUATION OUTER RETINAL BANDS ON OPTICAL COHERENCE TOMOGRAPHY FOLLOWING MACULAR EDEMA: A Possible Manifestation of Photoreceptor Misalignment

PURPOSE

Macular edema is a common retinal disease which may leave important anatomical and functional sequelaes. Directional fundus imaging consists of comparing on- and off-axis images to reveal angle-dependent reflectance properties of fundus structures, which may be related to misaligned photoreceptors. Here, we analyzed directional optical coherence tomography (OCT) and flood-illumination adaptive optics ophthalmoscopy images to detect evidence of misaligned photoreceptors following macular edema.

METHODS

Transversal, observational study. Nine patients having recovered a normal macular profile after macular edema due to retinal vein occlusion were included. For each patient, a reference OCT scan (i.e., with the incident beam normal to the fovea) was acquired, and off-axis scans were then acquired by laterally shifting the entry pupil. In addition, in four of these eyes, directional adaptive optics ophthalmoscopy documented the directional variations of cone metrics.

RESULTS

Included patients comprised two women and seven men (age range, 19-76 years). Reference OCT scans showed patchy attenuation of the cone outer segment tips and to a lesser extent of the inner segment/outer segment lines in all, but two eyes; these. Increased intensity of the cone outer segment tips and inner segment/outer segment lines could be observed on off-axis scans. Accordingly, fusion images showed 66% average reduction of the length of cone outer segment tips attenuation. In two cases, although reference scans showed continuity of outer bands, focal attenuation was evidenced in off-axis images. Directional adaptive optics ophthalmoscopy imaging showed a strong directional variability of cone counts in these areas, ranging from near absence to roughly two-third of reference values. In each case, directional variations of cone counts paralleled those of the reflectance of outer bands.

CONCLUSION

After macular edema, focal attenuations of the inner segment/outer segment and of the cone outer segment tips lines may be present on OCT. These areas may show a strong directional variability by both OCT and adaptive optics ophthalmoscopy, suggesting that misaligned photoreceptor outer segments contribute to such features. The evaluation of outer retinal damage following macular edema should therefore take into account the optical Stiles-Crawford effect to disambiguate missing from misaligned cones.

Cellular imaging of inherited retinal diseases using adaptive optics

Adaptive optics (AO) is an insightful tool that has been increasingly applied to existing imaging systems for viewing the retina at a cellular level. By correcting for individual optical aberrations, AO offers an improvement in transverse resolution from 10-15 μm to ~2 μm, enabling assessment of individual retinal cell types. One of the settings in which its utility has been recognised is that of the inherited retinal diseases (IRDs), the genetic and clinical heterogeneity of which warrants better cellular characterisation. In this review, we provide a summary of the basic principles of AO, its integration into multiple retinal imaging modalities and its clinical applications, focusing primarily on IRDs. Furthermore, we present a comprehensive summary of AO-based cellular findings in IRDs according to their associated disease-causing genes.

Use of focus measure operators for characterization of flood illumination adaptive optics ophthalmoscopy image quality

Adaptive optics flood illumination ophthalmoscopy (AO-FIO) allows imaging of the cone photoreceptor in the living human retina. However, clinical interpretation of the AO-FIO image remains challenging due to suboptimal quality arising from residual uncorrected wavefront aberrations and rapid eye motion. An objective method of assessing image quality is necessary to determine whether an AO-FIO image is suitable for grading and diagnostic purpose. In this work, we explore the use of focus measure operators as a surrogate measure of AO-FIO image quality. A set of operators are tested on data sets acquired at different focal depths and different retinal locations from healthy volunteers. Our results demonstrate differences in focus measure operator performance in quantifying AO-FIO image quality. Further, we discuss the potential application of the selected focus operators in (i) selection of the best quality AO-FIO image from a series of images collected at the same retinal location and (ii) assessment of longitudinal changes in the diseased retina. Focus function could be incorporated into real-time AO-FIO image processing and provide an initial automated quality assessment during image acquisition or reading center grading.

Photoreceptor-Based Biomarkers in AOSLO Retinal Imaging

Improved understanding of the mechanisms underlying inherited retinal degenerations has created the possibility of developing much needed treatments for these relentless, blinding diseases. However, standard clinical indicators of retinal health (such as visual acuity and visual field sensitivity) are insensitive measures of photoreceptor survival. In many retinal degenerations, significant photoreceptor loss must occur before measurable differences in visual function are observed. Thus, there is a recognized need for more sensitive outcome measures to assess therapeutic efficacy as numerous clinical trials are getting underway. Adaptive optics (AO) retinal imaging techniques correct for the monochromatic aberrations of the eye and can be used to provide nearly diffraction-limited images of the retina. Many groups routinely are using AO imaging tools to obtain in vivo images of the rod and cone photoreceptor mosaic, and it now is possible to monitor photoreceptor structure over time with single cell resolution. Highlighting recent work using AO scanning light ophthalmoscopy (AOSLO) across a range of patient populations, we review the development of photoreceptor-based metrics (e.g., density/geometry, reflectivity, and size) as candidate biomarkers. Going forward, there is a need for further development of automated tools and normative databases, with the latter facilitating the comparison of data sets across research groups and devices. Ongoing and future clinical trials for inherited retinal diseases will benefit from the improved resolution and sensitivity that multimodal AO retinal imaging affords to evaluate safety and efficacy of emerging therapies.

Automated measurements of human cone photoreceptor density in healthy and degenerative retina by region-based segmentation

Purpose

The purpose of this study was to develop an algorithm based on region-based segmentation for automated calculations of human cone photoreceptor density of en face images obtained by an adaptive optics scanning laser ophthalmoscope (AOSLO).

Subjects and methods

Cone mosaics of 15 eyes of 15 healthy subjects were photographed by a custom-built AOSLO. The cone density was calculated at 0.5, 1.0, and 1.5 mm temporal from the fovea using a region-based segmentation method (RSM) developed in our laboratory. The cone density was also determined by a manual identification method (MIM) and a conventional spatial filtering method (SFM). The cone densities of three eyes of three patients with retinal degeneration were calculated by the three methods and compared to the results from normal eyes.

Results

The cone densities in healthy retinas determined by the RSM at 0.5, 1.0, and 1.5 mm temporal from the fovea were 28,436, 21,233, and 13,620 cells/mm², respectively. These densities were in good agreement with a histological study and with in vivo AOSLO studies. The cone densities determined by RSM were different from those determined by MIM with a difference of 5% in healthy eyes. In eyes with retinal degeneration, with the appropriate threshold-level settings or spatial frequency bandwidth, the cone density measured by MIM was significantly closer to that measured by RSM than by SFM.

Conclusion

These results suggest that our method is more stable than conventional methods in cases of non-periodical photoreceptor structures such as the affected retinal area. Our method can be used in the longitudinal follow-up of retinal degenerative diseases and to determine the effect of therapy.