Hyperspectral Imaging of the Retina: A Review

Naevi Characterization Using Hyperspectral Imaging: A Pilot Study

PURPOSE

The prevalence of choroidal naevi is common and has been found to be up to 10%. Little is known regarding the optical properties of choroidal naevi. A novel hyperspectral eye fundus camera was used to investigate choroidal naevi's optical density spectra in the retina.

METHODS

In an ophthalmology clinic setting, patients with choroidal naevi were included in the study. Visual acuity and pressure were tested. Following mydriatics, optical coherence tomography and fundus photography were taken as a reference, after which a hyperspectral image with 12 nm spectral resolution at 450-700 nm was taken. The optical density spectra was measured across the area of the naevus.

RESULTS

Nine patients with 11 naevi were examined. The visual acuity was not affected by any of the naevi. All the naevi were flat as measured either with the optical coherence tomography and/or on inspection, and only one naevi had a risk factor (orange pigmentation). The Wasserstein distance between the background and the naevi was higher at 695 nm compared to 555 nm (p = .002). The naevi could be grouped into three clusters based on the extracted optical density spectra.

CONCLUSION

Choroidal naevi are better visible in longer wavelengths compared to shorter wavelengths. This finding can be used to contour and follow choroidal naevi. Choroidal naevi expose different optical density spectra that can be grouped into three different clusters. One of these clusters has an optical density spectra resembling the absorption spectra of lipofuscin, which may indicate the content of this pigment.

Hyperspectral retinal imaging biomarkers of ocular and systemic diseases

Hyperspectral imaging is a frontier in the field of medical imaging technology. It enables the simultaneous collection of spectroscopic and spatial data. Structural and physiological information encoded in these data can be used to identify and localise typically elusive biomarkers. Studies of retinal hyperspectral imaging have provided novel insights into disease pathophysiology and new ways of non-invasive diagnosis and monitoring of retinal and systemic diseases. This review provides a concise overview of recent advances in retinal hyperspectral imaging.

A High-Resolution Hyperspectral Imaging System for the Retina

In this study, we developed an imaging system that can acquire and produce high-resolution hyperspectral images of the retina. Our system combines the view from a high-resolution RGB camera and a snapshot hyperspectral camera together. The method is fast and can be constructed into a compact imaging device. We tested our system by imaging a calibrated color chart, biological tissues ex vivo, and a phantom of the human retina. By using image pansharpening methods, we were able to produce a high-resolution hyperspectral image. The images from the hyperspectral camera alone have a spatial resolution of 0.2 mm/pixel, whereas the pansharpened images have a spatial resolution of 0.1 mm/pixel, a 2x increase in spatial resolution. Our method has the potential to capture images of the retina rapidly. Our method preserves both the spatial and spectral fidelity, as shown by comparing the original hyperspectral images with the pansharpened images. The high-resolution hyperspectral imaging device can have a variety of applications in retina examinations.

Targeted spectroscopy in the eye fundus

Significance

The assessment of biomarkers in the eye is rapidly gaining traction for the screening, diagnosis, and monitoring of ocular and neurological diseases. Targeted ocular spectroscopy is a technology that enables concurrent imaging of the eye fundus and analysis of high-quality spectra from a targeted region within the imaged area. This provides structural, compositional, and functional information of specific regions of the eye fundus from a non-invasive approach to ocular biomarker detection.

Aim

The aim of our study was to demonstrate the multimodal functionality and validation of targeted ocular spectroscopy. This was done in vitro, using a reference target and a model eye, and in vivo.

Approach

Images and spectra from different regions of a reference target and a model eye were acquired and analyzed to validate the system. Targeted ocular fluorescence spectroscopy was also demonstrated with the same model. Subsequently, in vivo imaging and diffuse reflectance spectra were acquired to assess blood oxygen saturation in the optic nerve head and the parafovea of healthy subjects.

Results

Tests conducted with the reference target showed accurate spectral analysis within specific areas of the imaging space. In the model eye, distinct spectral signatures were observed for the optic disc, blood vessels, the retina, and the macula, consistent with the variations in tissue composition and functions between these regions. An ocular oximetry algorithm was applied to in vivo spectra from the optic nerve head and parafovea of healthy patients, showing significant differences in blood oxygen saturation. Finally, targeted fluorescence spectral analysis was performed in vitro.

Conclusions

Diffuse reflectance and fluorescence spectroscopy in specific regions of the eye fundus open the door to a whole new range of monitoring and diagnostic capabilities, from assessment of oxygenation in glaucoma and diabetic retinopathy to photo-oxidation and photodegradation in age-related macular degeneration.

Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection

Brain surgery is one of the most common and effective treatments for brain tumour. However, neurosurgeons face the challenge of determining the boundaries of the tumour to achieve maximum resection, while avoiding damage to normal tissue that may cause neurological sequelae to patients. Hyperspectral (HS) imaging (HSI) has shown remarkable results as a diagnostic tool for tumour detection in different medical applications. In this work, we demonstrate, with a robust k-fold cross-validation approach, that HSI combined with the proposed processing framework is a promising intraoperative tool for in-vivo identification and delineation of brain tumours, including both primary (high-grade and low-grade) and secondary tumours. Analysis of the in-vivo brain database, consisting of 61 HS images from 34 different patients, achieve a highest median macro F1-Score result of 70.2 ± 7.9% on the test set using both spectral and spatial information. Here, we provide a benchmark based on machine learning for further developments in the field of in-vivo brain tumour detection and delineation using hyperspectral imaging to be used as a real-time decision support tool during neurosurgical workflows.

Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies

The retina and brain share similar neurochemistry and neurodevelopmental origins, with the retina, often viewed as a "window to the brain." With retinal measures of structure and function becoming easier to obtain in clinical populations there is a growing interest in using retinal findings as potential biomarkers for disorders affecting the central nervous system. Functional retinal biomarkers, such as the electroretinogram, show promise in neurological disorders, despite having limitations imposed by the existence of overlapping genetic markers, clinical traits or the effects of medications that may reduce their specificity in some conditions. This narrative review summarizes the principal functional retinal findings in central nervous system disorders and related mouse models and provides a background to the main excitatory and inhibitory retinal neurotransmitters that have been implicated to explain the visual electrophysiological findings. These changes in retinal neurochemistry may contribute to our understanding of these conditions based on the findings of retinal electrophysiological tests such as the flash, pattern, multifocal electroretinograms, and electro-oculogram. It is likely that future applications of signal analysis and machine learning algorithms will offer new insights into the pathophysiology, classification, and progression of these clinical disorders including autism, attention deficit/hyperactivity disorder, bipolar disorder, schizophrenia, depression, Parkinson's, and Alzheimer's disease. New clinical applications of visual electrophysiology to this field may lead to earlier, more accurate diagnoses and better targeted therapeutic interventions benefiting individual patients and clinicians managing these individuals and their families.

Deformable registration of multimodal retinal images using a weakly supervised deep learning approach

There are different retinal vascular imaging modalities widely used in clinical practice to diagnose different retinal pathologies. The joint analysis of these multimodal images is of increasing interest since each of them provides common and complementary visual information. However, if we want to facilitate the comparison of two images, obtained with different techniques and containing the same retinal region of interest, it will be necessary to make a previous registration of both images. Here, we present a weakly supervised deep learning methodology for robust deformable registration of multimodal retinal images, which is applied to implement a method for the registration of fluorescein angiography (FA) and optical coherence tomography angiography (OCTA) images. This methodology is strongly inspired by VoxelMorph, a general unsupervised deep learning framework of the state of the art for deformable registration of unimodal medical images. The method was evaluated in a public dataset with 172 pairs of FA and superficial plexus OCTA images. The degree of alignment of the common information (blood vessels) and preservation of the non-common information (image background) in the transformed image were measured using the Dice coefficient (DC) and zero-normalized cross-correlation (ZNCC), respectively. The average values of the mentioned metrics, including the standard deviations, were DC = 0.72  ±  0.10 and ZNCC = 0.82  ±  0.04. The time required to obtain each pair of registered images was 0.12 s. These results outperform rigid and deformable registration methods with which our method was compared.

Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis

Currently, one of the most common causes of death worldwide is cancer. The development of innovative methods to support the early and accurate detection of cancers is required to increase the recovery rate of patients. Several studies have shown that medical Hyperspectral Imaging (HSI) combined with artificial intelligence algorithms is a powerful tool for cancer detection. Various preprocessing methods are commonly applied to hyperspectral data to improve the performance of the algorithms. However, there is currently no standard for these methods, and no studies have compared them so far in the medical field. In this work, we evaluated different combinations of preprocessing steps, including spatial and spectral smoothing, Min-Max scaling, Standard Normal Variate normalization, and a median spatial smoothing technique, with the goal of improving tumor detection in three different HSI databases concerning colorectal, esophagogastric, and brain cancers. Two machine learning and deep learning models were used to perform the pixel-wise classification. The results showed that the choice of preprocessing method affects the performance of tumor identification. The method that showed slightly better results with respect to identifing colorectal tumors was Median Filter preprocessing (0.94 of area under the curve). On the other hand, esophagogastric and brain tumors were more accurately identified using Min-Max scaling preprocessing (0.93 and 0.92 of area under the curve, respectively). However, it is observed that the Median Filter method smooths sharp spectral features, resulting in high variability in the classification performance. Therefore, based on these results, obtained with different databases acquired by different HSI instrumentation, the most relevant preprocessing technique identified in this work is Min-Max scaling.

Ophthalmic imaging in diabetic retinopathy: A review

Retinal imaging has been a key tool in the diagnosis, evaluation, management and documentation of diabetic retinopathy (DR) and diabetic macular oedema (DMO) for many decades. Imaging technologies have rapidly evolved over the last few decades, yielding images with higher resolution and contrast with less time, effort and invasiveness. While many retinal imaging technologies provide detailed insight into retinal structure such as colour reflectance photography and optical coherence tomography (OCT), others such as fluorescein or OCT angiography and oximetry provide dynamic and functional information. Many other novel imaging technologies are in development and are poised to further enhance our evaluation of patients with DR.

Adaptive denoising hyperspectral data for visualization enhancement of intraoperative tissue

The vast amount of reflectance information obtained from the hyperspectral imaging devices offers great opportunities for investigating the function and structure of human tissue. However, the captured hyperspectral data often contain various noises due to the intrinsic imperfection of associated electrical and optical imaging components. This work proposed an automatic total variation algorithm to suppress the noises while preserving the details of the spectral and spatial information. The variation of spectral images at neighboring bands was calculated for regulating the total variation of hyperspectral data so that the spectral-dependent noises can be treated differentially across all bands. Experimental results demonstrated that the proposed method could effectively remove the spectral noises, especially near the ends of those extreme bands. The noise suppressed hyperspectral data could then be used for the visualization enhancement on pathophysiological conditions of intraoperative observed anatomies such as the vessels of brain tissues.

Topical Review: Studies of Ocular Function and Disease Using Hyperspectral Imaging

SIGNIFICANCE

Advances in imaging technology over the last two decades have produced significant innovations in medical imaging. Hyperspectral imaging (HSI) is one of these innovations, enabling powerful new imaging tools for clinical use and greater understanding of tissue optical properties and mechanisms underlying eye disease.Hyperspectral imaging is an important and rapidly growing area in medical imaging, making possible the concurrent collection of spectroscopic and spatial information that is usually obtained from separate optical recordings. In this review, we describe several mainstream techniques used in HSI, along with noteworthy advances in optical technology that enabled modern HSI techniques. Presented also are recent applications of HSI for basic and applied eye research, which include a novel method for assessing dry eye syndrome, clinical slit-lamp examination of corneal injury, measurement of blood oxygen saturation in retinal disease, molecular changes in macular degeneration, and detection of early stages of Alzheimer disease. The review also highlights work resulting from integration of HSI with other imaging tools such as optical coherence tomography and autofluorescence microscopy and discusses the adaptation of HSI for clinical work where eye motion is present. Here, we present the background and main findings from each of these reports along with specific references for additional details.

VNIR-NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection

Currently, intraoperative guidance tools used for brain tumor resection assistance during surgery have several limitations. Hyperspectral (HS) imaging is arising as a novel imaging technique that could offer new capabilities to delineate brain tumor tissue in surgical-time. However, the HS acquisition systems have some limitations regarding spatial and spectral resolution depending on the spectral range to be captured. Image fusion techniques combine information from different sensors to obtain an HS cube with improved spatial and spectral resolution. This paper describes the contributions to HS image fusion using two push-broom HS cameras, covering the visual and near-infrared (VNIR) [400–1000 nm] and near-infrared (NIR) [900–1700 nm] spectral ranges, which are integrated into an intraoperative HS acquisition system developed to delineate brain tumor tissue during neurosurgical procedures. Both HS images were registered using intensity-based and feature-based techniques with different geometric transformations to perform the HS image fusion, obtaining an HS cube with wide spectral range [435–1638 nm]. Four HS datasets were captured to verify the image registration and the fusion process. Moreover, segmentation and classification methods were evaluated to compare the performance results between the use of the VNIR and NIR data, independently, with respect to the fused data. The results reveal that the proposed methodology for fusing VNIR–NIR data improves the classification results up to 21% of accuracy with respect to the use of each data modality independently, depending on the targeted classification problem.

Past, present and future role of retinal imaging in neurodegenerative disease

Retinal imaging technology is rapidly advancing and can provide ever-increasing amounts of information about the structure, function and molecular composition of retinal tissue in humans in vivo. Most importantly, this information can be obtained rapidly, non-invasively and in many cases using Food and Drug Administration-approved devices that are commercially available. Technologies such as optical coherence tomography have dramatically changed our understanding of retinal disease and in many cases have significantly improved their clinical management. Since the retina is an extension of the brain and shares a common embryological origin with the central nervous system, there has also been intense interest in leveraging the expanding armamentarium of retinal imaging technology to understand, diagnose and monitor neurological diseases. This is particularly appealing because of the high spatial resolution, relatively low-cost and wide availability of retinal imaging modalities such as fundus photography or OCT compared to brain imaging modalities such as magnetic resonance imaging or positron emission tomography. The purpose of this article is to review and synthesize current research about retinal imaging in neurodegenerative disease by providing examples from the literature and elaborating on limitations, challenges and future directions. We begin by providing a general background of the most relevant retinal imaging modalities to ensure that the reader has a foundation on which to understand the clinical studies that are subsequently discussed. We then review the application and results of retinal imaging methodologies to several prevalent neurodegenerative diseases where extensive work has been done including sporadic late onset Alzheimer's Disease, Parkinson's Disease and Huntington's Disease. We also discuss Autosomal Dominant Alzheimer's Disease and cerebrovascular small vessel disease, where the application of retinal imaging holds promise but data is currently scarce. Although cerebrovascular disease is not generally considered a neurodegenerative process, it is both a confounder and contributor to neurodegenerative disease processes that requires more attention. Finally, we discuss ongoing efforts to overcome the limitations in the field and unmet clinical and scientific needs.

The role of neuroinflammation in the pathogenesis of glaucoma neurodegeneration

The chapter is a review enclosed in the volume "Glaucoma: A pancitopatia of the retina and beyond." No cure exists for glaucoma. Knowledge on the molecular and cellular alterations underlying glaucoma neurodegeneration (GL-ND) includes innovative and path-breaking research on neuroinflammation and neuroprotection. A series of events involving immune response (IR), oxidative stress and gene expression are occurring during the glaucoma course. Uveitic glaucoma (UG) is a prevalent acute/chronic complication, in the setting of chronic anterior chamber inflammation. Managing the disease requires a team approach to guarantee better results for eyes and vision. Advances in biomedicine/biotechnology are driving a tremendous revolution in ophthalmology and ophthalmic research. New diagnostic and imaging modalities, constantly refined, enable outstanding criteria for delimiting glaucomatous neurodegeneration. Moreover, biotherapies that may modulate or inhibit the IR must be considered among the first-line for glaucoma neuroprotection. This review offers the readers useful and practical information on the latest updates in this regard.