Optical coherence tomography

Generative adversarial networks in medical image reconstruction: A systematic literature review

PURPOSE

Recent advancements in generative adversarial networks (GANs) have demonstrated substantial potential in medical image processing. Despite this progress, reconstructing images from incomplete data remains a challenge, impacting image quality. This systematic literature review explores the use of GANs in enhancing and reconstructing medical imaging data.

METHOD

A document survey of computing literature was conducted using the ACM Digital Library to identify relevant articles from journals and conference proceedings using keyword combinations, such as "generative adversarial networks or generative adversarial network," "medical image or medical imaging," and "image reconstruction."

RESULTS

Across the reviewed articles, there were 122 datasets used in 175 instances, 89 top metrics employed 335 times, 10 different tasks with a total count of 173, 31 distinct organs featured in 119 instances, and 18 modalities utilized in 121 instances, collectively depicting significant utilization of GANs in medical imaging. The adaptability and efficacy of GANs were showcased across diverse medical tasks, organs, and modalities, utilizing top public as well as private/synthetic datasets for disease diagnosis, including the identification of conditions like cancer in different anatomical regions. The study emphasized GAN's increasing integration and adaptability in diverse radiology modalities, showcasing their transformative impact on diagnostic techniques, including cross-modality tasks. The intricate interplay between network size, batch size, and loss function refinement significantly impacts GAN's performance, although challenges in training persist.

CONCLUSIONS

The study underscores GANs as dynamic tools shaping medical imaging, contributing significantly to image quality, training methodologies, and overall medical advancements, positioning them as substantial components driving medical advancements.

Optical coherence tomography for noninvasive monitoring of drug delivery

Optical Coherence Tomography (OCT) has revolutionized various medical imaging and diagnostics fields, offering unprecedented insights into the microstructural compositions of biological tissues. In recent years, OCT applications have been extended to noninvasive drug delivery monitoring, which is a critical aspect of many therapeutic procedures and pharmacokinetic studies. Such an extension is strongly enhanced by the inherent combination with 3D anatomical images provided by OCT. This review presents an overview of the principles of OCT technology, its functional extensions for drug delivery systems, and its advancements in monitoring therapeutic interventions. We discuss its advantages over traditional imaging modalities in terms of spatial resolution, depth penetration, and real-time capabilities. The paper highlights significant studies that have utilized OCT for the visualization and quantification of drug delivery processes, including the diffusion of injectable formulations in ocular tissues and the permeation of topical drugs through the skin. In the review, we focused on the latest OCT applications, including OCT-guided drug injection, topical drug delivery monitoring, application of OCT in inhaled drug delivery systems, and the integration of OCT with other imaging modalities.

Development and validation of a multi-stage self-supervised learning model for optical coherence tomography image classification

OBJECTIVE

This study aimed to develop a novel multi-stage self-supervised learning model tailored for the accurate classification of optical coherence tomography (OCT) images in ophthalmology reducing reliance on costly labeled datasets while maintaining high diagnostic accuracy.

MATERIALS AND METHODS

A private dataset of 2719 OCT images from 493 patients was employed, along with 3 public datasets comprising 84 484 images from 4686 patients, 3231 images from 45 patients, and 572 images. Extensive internal, external, and clinical validation were performed to assess model performance. Grad-CAM was employed for qualitative analysis to interpret the model's decisions by highlighting relevant areas. Subsampling analyses evaluated the model's robustness with varying labeled data availability.

RESULTS

The proposed model outperformed conventional supervised or self-supervised learning-based models, achieving state-of-the-art results across 3 public datasets. In a clinical validation, the model exhibited up to 17.50% higher accuracy and 17.53% higher macro F-1 score than a supervised learning-based model under limited training data.

DISCUSSION

The model's robustness in OCT image classification underscores the potential of the multi-stage self-supervised learning to address challenges associated with limited labeled data. The availability of source codes and pre-trained models promotes the use of this model in a variety of clinical settings, facilitating broader adoption.

CONCLUSION

This model offers a promising solution for advancing OCT image classification, achieving high accuracy while reducing the cost of extensive expert annotation and potentially streamlining clinical workflows, thereby supporting more efficient patient management.

Detection of Ocular Surface Squamous Neoplasia Using Artificial Intelligence With Anterior Segment Optical Coherence Tomography

PURPOSE

To develop and validate a deep learning (DL) model to differentiate ocular surface squamous neoplasia (OSSN) from pterygium and pinguecula using high-resolution anterior segment optical coherence tomography (AS-OCT).

DESIGN

Retrospective Diagnostic Accuracy Study.

METHODS

Setting: Single-center.

STUDY POPULATION

All eyes with a clinical or biopsy-proven diagnosis of OSSN, pterygium, or pinguecula that received AS-OCT imaging.

PROCEDURES

Imaging data was extracted from Optovue AS-OCT (Fremont, CA) and patients' clinical or biopsy-proven diagnoses were collected from electronic medical records. A DL classification model was developed using two methodologies: (1) a masked autoencoder was trained with unlabeled data from 105,859 AS-OCT images of 5746 eyes and (2) a Vision Transformer supervised model coupled to the autoencoder used labeled data for fine-tuning a binary classifier (OSSN vs non-OSSN lesions). A sample of 2022 AS-OCT images from 523 eyes (427 patients) were classified by expert graders into "OSSN or suspicious for OSSN" and "pterygium or pinguecula." The algorithm's diagnostic performance was evaluated in a separate test sample using 566 scans (62 eyes, 48 patients) with biopsy-proven OSSN and compared with expert clinicians who were masked to the diagnosis. Analysis was conducted at the scan-level for both the DL model and expert clinicians, who were not provided with clinical images or supporting clinical data.

MAIN OUTCOME

Diagnostic performance of expert clinicians and the DL model in identifying OSSN on AS-OCT scans.

RESULTS

The DL model had an accuracy of 90.3% (95% confidence intervals [CI]: 87.5%-92.6%), with sensitivity of 86.4% (95% CI: 81.4%-90.4%) and specificity of 93.2% (95% CI: 89.9%-95.7%) compared to the biopsy-proven diagnosis. Expert graders had a lower sensitivity 69.8% (95% CI: 63.6%-75.5%) and slightly higher specificity 98.5% (95% CI: 96.4%-99.5%) than the DL model. The area under the receiver operating characteristic curve for the DL model was 0.945 (95% CI: 0.918-0.972) and significantly greater than expert graders (area under the receiver operating characteristic curve = 0.688, P < .001).

CONCLUSIONS

A DL model applied to AS-OCT scans demonstrated high accuracy, sensitivity, and specificity in differentiating OSSN from pterygium and pinguecula. Interestingly, the model had comparable diagnostic performance to expert clinicians in this study and shows promise for enhancing clinical decision-making. Further research is warranted to explore the integration of this artificial intelligence-driven approach in routine screening and diagnostic protocols for OSSN.

In-situ forming solvent-induced phase inversion implants for controlled drug delivery: Role of hydrophilic polymers

In recent years, there has been a surge of research focused on in situ-forming implants as a method of localized drug delivery. Despite advancements, the predominant challenge in situ-forming solvent-induced phase inversion (SIPI) implants is significant burst release which typically occurs within the first 24 h post-administration. Another notable challenge is the real-time characterization of these implants, which is crucial for understanding their in situ formation and degradation mechanism. This study explores the impact of various hydrophilic polymers-hydroxypropyl methylcellulose (HPMC), hydroxypropyl cellulose (HPC), Carbopol, and carboxymethylcellulose (CMC) - on implant formation and drug release. SIPI systems, which are commonly composed of poly(lactic-co-glycolic acid) (PLGA) and N-methyl pyrrolidone (NMP), offer localized, controlled drug release but suffer from an initial burst within 24 h post-administration. The incorporation of hydrophilic polymers aims to modulate this release and improve implant properties. For first-time, optical coherence tomography (OCT) imaging was employed for non-invasive assessment of the rate of in situ phase inversion and the resulting implant morphology, whereas scanning electron microscopy (SEM) and digital microscopy provided further insights into the internal structure of the implants. The results demonstrated that the inclusion of polymers such as HPMC and Carbopol effectively reduced burst release, whereas polymers such as HPC and CMC exhibited faster phase inversion, resulting in a more porous implant morphology and greater burst release. Additionally, the mechanical properties and mucoadhesive capabilities of the formulations were tested, suggesting that Carbopol-enhanced implants are particularly suitable for applications requiring prolonged retention at mucosal sites. This investigation provides critical insights into the design and optimization of SIPI systems for drug delivery.

Investigations of injection strategies to use heparinized normal saline instead of contrast media for intracoronary optical coherence tomography imaging

BackgroundThe benefits of intravascular imaging-guided percutaneous coronary interventions (PCI) are well established. Intravascular imaging guidance improves short- and long-term outcomes, especially in complex PCI. Optical coherence tomography (OCT) has a higher resolution than intravascular ultrasound. However, the usage of OCT is mainly limited by the need to use contrast for flushing injections, which increases the risk of contrast-induced acute kidney injury, especially in patients with underlying chronic kidney disease. The aim of this study was to prove that flushing techniques with normal saline instead of contrast can be used in OCT imaging and can generate high-quality images.MethodsThis prospective single-center observational study included patients with indications for OCT-guided PCI. For OCT pullbacks, heparinized saline was injected by an automatic pump injector at different rates, and additional extension catheters for selective coronary artery engagement were used at the operator's discretion. Recordings were made using the Ilumien Optis OCT system (Abbott) and the Dragonfly (Abbott) catheter and were analyzed at 1-mm intervals by two operators. Pullbacks were categorized as having optimal, acceptable, or unacceptable imaging quality. A clinically usable run was determined if >75% of the region of interest length was described as having optimal or acceptable imaging quality.ResultsA total of 32 patients were enrolled in the study; 47 different lesions were assessed before and after PCI. In total, 91.5% of runs were described as clinically suitable for use.ConclusionHeparinized saline injections for OCT imaging are effective in generating good-quality OCT images suitable for clinical use.

Artificial intelligence in managing retinal disease-current concepts and relevant aspects for health care providers

Given how the diagnosis and management of many ocular and, most specifically, retinal diseases heavily rely on various imaging modalities, the introduction of artificial intelligence (AI) into this field has been a logical, inevitable, and successful development in recent decades. The field of retinal diseases has practically become a showcase for the use of AI in medicine. In this article, after providing a short overview of the most relevant retinal diseases and their socioeconomic impact, we highlight various aspects of how AI can be applied in research, diagnosis, and disease management and how this is expected to alter patient flows, affecting also health care professionals beyond ophthalmologists.

Retinal Ischemic Perivascular Lesions (RIPLs) as Potential Biomarkers for Systemic Vascular Diseases: A Narrative Review of the Literature

Retinal ischemic perivascular lesions (RIPLs) are characteristic focal thinning of the inner nuclear layer, with an upward expansion of the outer nuclear layer identified by spectral domain optical coherence tomography (SD-OCT), causing a focal irregular appearance of the middle retina. RIPLs result from retinal hypoperfusion in the deep capillary plexus, as a legacy of paracentral acute middle maculopathy, representing permanent anatomical markers of prior ischemic events. Although frequently found incidentally during routine eye examinations, RIPLs may provide insights into subclinical vascular damage that underpins various cardio- and cerebrovascular diseases. The aim of this narrative review is to summarize the relationships of RIPLs with retinal and systemic vascular diseases, including arterial hypertension, coronary artery disease, carotid artery stenosis, atrial fibrillation, stroke, sickle cell disease, and diabetes mellitus. Cardiovascular and metabolic diseases, which are the leading causes of morbidity and mortality worldwide, often remain asymptomatic for years despite early structural changes until severe adverse events occur. Noninvasive retinal biomarkers such as RIPLs, which are readily and noninvasively detected through SD-OCT scans, could help in the early detection and stratification of patients at risk for cardiovascular diseases, facilitate timely medical interventions and lifestyle changes, and ultimately improve disease prevention in a "personalized medicine" approach. While further research is needed to establish the prevalence of RIPLs in the general population and their full clinical significance, advances in ophthalmic imaging technologies combined with rapid progress in artificial intelligence applications in medical research could accelerate the development of RIPLs in retinal imaging-based oculomics.

Optical coherence tomography surpasses fundus imaging and intracranial pressure measurement in monitoring idiopathic intracranial hypertension

We aim to evaluate the retinal nerve fiber layer (RNFL) thickness measured with optical coherence tomography (OCT) in comparison with papilledema grade, and to assess the relationship between RNFL thickness, papilledema grade, and intracranial pressure (ICP) in idiopathic intracranial hypertension (IIH). Sixty-five patients with active IIH (AIIH) with papilledema, 39 with chronic IIH (CIIH) without papilledema and 80 healthy controls (HC) were examined with OCT and fundus imaging. RNFL thickness, papilledema grade and ICP level were assessed in 55 with AIIH and 26 with CIIH. RNFL thickness was significantly higher in AIIH compared to CIIH or HC. RNFL thickness correlated strongly with papilledema grade (coefficient 0.78, p < 0.01) and moderately with ICP (coefficient 0.569, p < 0.01). RNFL thickness was associated with papilledema progression (R2 = 0.656, p < 0.01): specifically, with increases of 9 µm from normal to mild grade (p > 0.05), 91 µm from normal to moderate (p < 0.01), and 214 µm from normal to severe (p < 0.01). ICP showed a weaker correlation with papilledema grades (R2 = 0.339, p < 0.05), with significant increase (8 cm H2O, p < 0.01) only from normal to severe papilledema. RNFL correlated strongly with papilledema grade and moderately with ICP levels. RNFL thickness increased proportionally per papilledema grade.

Diagnosing neurodegenerative disorders using retina as an external window: A systematic review of OCT-MRI correlations

BackgroundRecent studies have explored optical coherence tomography (OCT) and OCT-angiography (OCT-A) as biomarkers for Alzheimer's disease (AD). However, correlations between OCT/OCT-A and neurodegeneration metrics remain underexplored.ObjectiveWe performed a systematic review of OCT/OCT-A and structural brain imaging using MRI across various neurodegenerative disorders.MethodsWe searched Medline, Embase, and various other databases from January to June 2023 using keywords regarding neurodegenerative conditions and OCT/OCT-A. Out of 2962 citations. 93 articles were reviewed, and 28 met our inclusion criteria.ResultsLayer-or-region-specific retinal metrics were the most promising for non-vascular neurodegeneration, while vascular retinal parameters had the unique capacity to reflect vascular lesions. Both types of biomarkers correlated with global brain atrophy. Microstructural brain alterations best correlated with layer-specific thinning of retina.ConclusionsA better understanding of associations between retinal and brain lesions could eventually lead to the clinical application of retinal biomarkers for the early diagnosis of neurodegenerative conditions.

Keeping an eye on Parkinson's disease: color vision and outer retinal thickness as simple and non-invasive biomarkers

Over the last two decades, visual symptoms and retinal changes in Parkinson's disease (PD) have emerged as important biomarkers. Color vision deficiency, which begins in the outer retina, has been increasingly investigated, but a focused review of these papers has not recently been conducted. Similarly, thinning of the outer retina as measured using optical coherence tomography (OCT) holds potential as a screening marker for PD, particularly as these devices are already commonplace in community and hospital settings. Moreover, outer retinal thinning may be more specific for Parkinson's disease as inner retinal changes also occur in more common neurodegenerative diseases like glaucoma and Alzheimer's disease. This review summarizes contemporary evidence on two outer retina focused measures, color vision and outer retinal thickness, which can be readily quantified using non-invasive approaches and thus examines their potential as biomarkers for screening, detection, and progression in PD.

Macular curvature in vertical cross sections of optical coherence tomography in school age children

Although the underlying mechanism of macular curvature remains unclear, it has been linked to various ocular diseases. However, changes in macular curvature during growth have not been thoroughly investigated. This study aimed to investigate macular curvature in children of different ages and its association with axial length. A total of 122 right eyes of healthy elementary school students (Group E; 8-9 years old; 61 males and 61 females) and 173 right eyes of healthy junior high school students (Group J; 12-13 years old; 83 males and 90 females) were included. Axial length, color fundus photographs, and optical coherence tomographic vertical cross-sectional images of the macula were obtained and used for analysis. The macular curvature was plotted as the retinal pigment epithelium and fitted to a second-degree polynomial equation using ImageJ software, to calculate the macular curvature. The Mann-Whitney U test compared macular curvature and axial length between the E and J Groups. The association between the macular curvature and axial length was determined using Spearman's correlation analysis. Group J's axial length and macular curvature were significantly greater than Group E's (p < 0.001). Macular curvature was significantly positively correlated with axial length in Group J (r = 0.40, P < 0.001) but not in Group E (r = 0.08, P = 0.40). These findings suggest a possible increase in macular steepness during this period. Further longitudinal cohort studies are needed to confirm these results.

Optical Coherence Tomography Velocimetry for In-Line Processing: Velocity Profiles and the Intermittency of Opaque Complex Fluids In Situ

We demonstrate optical coherence tomography (OCT) velocimetry with in-line processing of complex fluids for the first time. The OCT measurements were performed on a perspex section of a test rig containing ∼40 L of complex fluids, analogous to real-world manufacturing conditions. Opaque solutions of lamellar surfactant gel networks (LGNs) and powdered milk were explored. Velocity profiles characteristic of power law fluids were found in the LGNs, in good agreement with independent measurements of the flow rate and off-line determination of viscosity. The velocity fluctuations of 3.4 pL volumes of the fluids in the test rig were also explored. LGNs demonstrated smooth, steady flows, whereas the powdered milk demonstrated marked instability, both showing intermittent behavior and Kolmogorov scaling for fully developed classical turbulence of Newtonian fluids (P(ω) ∼ ω-5/3, where P(ω) is the power spectral density of the velocity fluctuations, and ω is the frequency). The effects of dynamic changes in formulation on velocimetry measurements could be observed with LGNs during the addition of salt and with the milk powder due to biofouling.

Advancements in the diagnosis of biliopancreatic diseases: A comparative review and study on future insights

Owing to the complex and often asymptomatic presentations, the diagnosis of biliopancreatic diseases, including pancreatic and biliary malignancies, remains challenging. Recent technological advancements have remarkably improved the diagnostic accuracy and patient outcomes in these diseases. This review explores key advancements in diagnostic modalities, including biomarkers, imaging techniques, and artificial intelligence (AI)-based technologies. Biomarkers, such as cancer antigen 19-9, KRAS mutations, and inflammatory markers, provide crucial insights into disease progression and treatment responses. Advanced imaging modalities include enhanced computed tomography (CT), positron emission tomography-CT, magnetic resonance cholangiopancreatography, and endoscopic ultrasound. AI integration in imaging and pathology has enhanced diagnostic precision through deep learning algorithms that analyze medical images, automate routine diagnostic tasks, and provide predictive analytics for personalized treatment strategies. The applications of these technologies are diverse, ranging from early cancer detection to therapeutic guidance and real-time imaging. Biomarker-based liquid biopsies and AI-assisted imaging tools are essential for non-invasive diagnostics and individualized patient management. Furthermore, AI-driven models are transforming disease stratification, thus enhancing risk assessment and decision-making. Future studies should explore standardizing biomarker validation, improving AI-driven diagnostics, and expanding the accessibility of advanced imaging technologies in resource-limited settings. The continued development of non-invasive diagnostic techniques and precision medicine approaches is crucial for optimizing the detection and management of biliopancreatic diseases. Collaborative efforts between clinicians, researchers, and industry stakeholders will be pivotal in applying these advancements in clinical practice.

Label-free evaluation of mouse embryo quality using time-lapse bright field and optical coherence microscopy

The selection of high-quality embryos is essential to enhance the implantation rate for in vitro fertilization (IVF). Optical coherence microscopy (OCM) can noninvasively provide three-dimensional (3D) high-resolution imaging of developing embryos. The revealed microstructures can be used for accurate embryo evaluation. Here, we acquire time-lapse 3D OCM images with co-registered bright-field imaging on mouse embryo development from the one-cell stage to the fully hatched blastocyst inside an incubator. Our results demonstrate the capability of OCM to detect structural features of the developing embryos. The second and third embryonic cell cycles are indicated to be associated with blastocyst formation and the hatching capability. OCM-based time-lapse technology holds the potential to enrich early embryo development insights and streamline embryo selection within IVF clinics.

Retinal vessel density and cognitive function in healthy older adults

The eye is considered a gateway to the brain, as the retina is the only tissue of the central nervous system not protected by bones. This enables non-invasive imaging to provide exceptional insights into the brain. Based on the similarity of brain and retinal structure, it is being investigated whether changes in retinal blood flow could serve as a potential biomarker for cognitive decline. Optical coherence tomography angiography (OCTA) enables an examination of the microcirculation of the retina on the basis of retinal flow registration. Retinal blood flow has been associated with cognitive function and changes have been shown for Alzheimer's patients, whereas data for healthy older subjects are contradictory. The aim of this study was to correlate OCTA parameters with the individual performance in tests assessing the executive functions (EFs) inhibition, updating and shifting in a group of healthy older adults (range: 65-79) using structural equation modeling, with hematocrit serving as a mediator. A model was obtained for vessel density (VD) of the retinal superficial vascular complex and the EF parameters inhibition and updating. The model revealed that only the mediator hematocrit correlated with EF, whereas neither the direct path VD to EF nor the indirect path VD via hematocrit to EF were significantly correlated. Regression analysis with hematocrit yielded significant results for the variable updating. We conclude that higher levels of hematocrit shorten reaction time and the coefficient of variation increases correspondingly, indicating a positive effect of hematocrit on EF.

Using Optical Coherence Tomography in Plant Biology Research: Review and Prospects

Visualizing the microscopic structure of plants in vivo, non-invasively, and in real-time is the Holy Grail of botany. Optical coherence tomography (OCT) has all the characteristics necessary to achieve this feat. Indeed, OCT provides volumetric images of the internal structure of plants without the need for histological preparation. With its micrometric resolution, OCT is commonly used in medicine, primarily in ophthalmology. But it is seldom used in the field of botany. The aim of the present work is thus to review the latest technical development in the field of OCT and to highlight its current use in botany, in order to promote the technique and further advance research in the field of botany.

Computer vision-guided rapid and precise automated cranial microsurgeries in mice

A common procedure that allows interfacing with the brain is cranial microsurgery, wherein small to large craniotomies are performed on the overlying skull for insertion of neural interfaces or implantation of optically clear windows for long-term cranial observation. Performing craniotomies requires skill, time, and precision to avoid damaging the brain and dura. Here, we present a computer vision-guided craniotomy robot (CV-Craniobot) that uses machine learning to accurately estimate the dorsal skull anatomy from optical coherence tomography images. Instantaneous information of skull morphology is used by a robotic mill to rapidly and precisely remove the skull from a desired craniotomy location. We show that the CV-Craniobot can perform small (2- to 4-millimeter diameter) craniotomies with near 100% success rates within 2 minutes and large craniotomies encompassing most of the dorsal cortex in less than 10 minutes. Thus, the CV-Craniobot enables rapid and precise craniotomies, reducing surgery time compared to human practitioners and eliminating the need for long training.

Recent advances in non-invasive in vivo tracking of cell-based cancer immunotherapies

Immunotherapy has been at the forefront of cancer treatment research in recent years due to an increased understanding of the immune system's role in cancer and the substantial benefits it has demonstrated compared to conventional treatment methods. In particular, immune cell-based approaches utilizing T cells, natural killer (NK) cells, macrophages, and more have shown great potential as cancer treatments. While these treatments hold promise, there are still numerous issues that limit their clinical translation, including a lack of understanding of their mechanisms and inconsistent responses to treatment. Traditionally, tissue or blood samples are collected as a means of monitoring treatment progression. However, these in vitro diagnostics are invasive and provide limited information about the real-time status of the treatment or its long-term effectiveness. To address these limitations, novel non-invasive imaging modalities have been developed. These include optical imaging, X-ray computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and single-photon emission computed tomography (SPECT), and photoacoustic (PA) imaging. This review focuses on methods for tracking cell-based cancer immunotherapies using these in vivo imaging modalities, thereby enhancing real-time monitoring of their therapeutic effect and predictions of their long-term efficacy.

Analysis on optical coherence tomography images to detect irregularities and restoration on paper-based artwork

Image analysis techniques were developed and used on optical coherence tomography (OCT) images for the analysis of restoration on paper-based artwork. Three etchings were imaged using an 840 nm portable OCT system from Lumedica and analysed using an algorithm developed to automatically detect restoration boundaries. The culmination of the algorithm was in its use for analysis on Landscape with an Obelisk, produced in the 1650s by Rembrandt van Rijn. The artwork is of interest due to the extensive restoration work conducted by art restorer Carl Schweidler in the 1920s, renowned for his restoration technique of carefully shaving the edges of both artwork and repair pieces before piecing them together perfectly. The data processing pipeline was optimised using three etchings that contain drastically different scales of restoration work so that the analysis algorithm may be used for future paper-based conservation studies.

Retinal OCT image segmentation with deep learning: A review of advances, datasets, and evaluation metrics

Optical coherence tomography (OCT) is a widely used imaging technology in ophthalmic clinical practice, providing non-invasive access to high-resolution retinal images. Segmentation of anatomical structures and pathological lesions in retinal OCT images, directly impacts clinical decisions. While commercial OCT devices segment multiple retinal layers in healthy eyes, their performance degrades severely under pathological conditions. In recent years, the rapid advancements in deep learning have significantly driven research in OCT image segmentation. This review provides a comprehensive overview of the latest developments in deep learning-based segmentation methods for retinal OCT images. Additionally, it summarizes the medical significance, publicly available datasets, and commonly used evaluation metrics in this field. The review also discusses the current challenges faced by the research community and highlights potential future directions.

Optical coherence tomography for label-free detection and characterization of methicillin-resistant S. aureus biofilms

Significance

Orthopedic implant-associated infections cause serious complications primarily attributed to bacterial biofilm formation and are often characterized by increased antibiotic resistance and diminished treatment response. Yet, no methods currently exist to identify biofilms intraoperatively-surgeons rely solely on their eyes and hands and cannot detect or differentiate infected tissue to determine the location and extent of contamination.

Aim

As the first step in addressing this unmet clinical need, here, we develop an optical coherence tomography (OCT)-based imaging method capable of detection in situ and quantification of one of the most dangerous orthopedic biofilms formed by methicillin-resistant Staphylococcus aureus (MRSA).

Approach

Growing biofilms on orthopedic hardware, we identify MRSA distinct optical signature through histogram-based multi-parametric texture analysis of OCT images and support the findings with bioluminescence imaging and scanning electron microscopy. Under identical experimental conditions, we identify an optical signature of Escherichia coli (E. coli) biofilms and use it to distinguish and quantify both species within MRSA-E. coli biofilms.

Results

The developed OCT-based methodology was successfully tested for (1) MRSA colonies delineation, (2) detection of metal hardware (an important feature for clinical translation where the metal surface of most orthopedic hardware is not flat), (3) automated quantification of biofilm thickness and roughness, and (4) identification of pores and, therefore, ability to evaluate the role of porosity-one of the critical biological metrics in relation to biofilm maturity and response to treatment. For the first time, we demonstrated complex pore structures of thick ( > 100    microns ) MRSA biofilms in situ with an unprecedented level of detail.

Conclusions

The proposed rapid noninvasive detection/quantification of MRSA biofilms on metal surfaces and delineation of their complex network of pores opens new venues for label-free MRSA detection in preclinical models of trauma surgery, expansion to other bacterial strains, and further clinical translation.

Rolling-phase dynamic full-field OCT

Dynamic full-field optical coherence tomography (DFFOCT) has recently emerged as an invaluable label-free microscopy technique, owing to its sensitivity to cell activity, as well as speed and sectioning ability. However, the quality of DFFOCT images is often degraded due to phase noise and fringe artifacts. In this work, we present a new implementation, to the best of our knowledge, named rolling-phase (RP) DFFOCT, in which the reference arm is slowly scanned over magnitudes exceeding 2π. We demonstrate mathematically and experimentally that it shows superior image quality while enabling to extract both static and dynamic contrast simultaneously. We showcase RP-DFFOCT on a macaque retinal explant and demonstrate its ability to better resolve subcellular structures, including intranuclear activity.

Visible-light optical coherence tomography and its applications

Visible-light optical coherence tomography (vis-OCT) is an emerging OCT technology that uses visible rather than near-infrared illumination and is useful for pre-clinical and clinical imaging. It provides one-micron level axial resolution and distinct scattering and absorption contrast that enables oximetry but requires additional considerations in system implementation and practical settings. We review the development of vis-OCT and demonstrated applications. We also provide insights into prospects and possible technological improvements that may address current challenges.

Nonperfused Retinal Capillaries-A New Method Developed on OCT and OCTA

Purpose

This study aims to develop a new method to quantify nonperfused retinal capillaries (NPCs) and evaluate NPCs in eyes with AMD and diabetic retinopathy (DR).

Methods

We averaged multiple registered optical coherence tomography (OCT)/OCT angiography (OCTA) scans to create high-definition volumes. The deep capillary plexus slab was defined and segmented. A developed deep learning denoising algorithm removed tissue background noise from capillaries in en face OCT/OCTA. The algorithm segmented NPCs by identifying capillaries from OCT without corresponding flow signals in OCTA. We then investigated the relationships between NPCs and known features in AMD and DR.

Results

The segmented NPC achieved an accuracy of 88.2% compared to manual grading of NPCs in DR. Compared to healthy controls, both the mean number and total length (mm) of NPCs was significantly increased in AMD and DR eyes (P < 0.001, P < 0.001). Compared to early and intermediate AMD, the number and total length of NPCs were significantly higher in advanced AMD (number: P < 0.001, P < 0.001; total length: P = 0.002, P = 0.003). Geography atrophy, macular neovascularization, drusen volume, and extrafoveal avascular area (EAA) significantly correlated with increased NPCs (P < 0.05). In DR eyes, NPCs correlated with the number of microaneurysms and EAA (P < 0.05). The presence of fluid did not significantly correlate with NPCs in AMD and DR.

Conclusions

A deep learning-based algorithm can segment and quantify retinal capillaries that lack flow using colocalized OCT/OCTA. This new biomarker may be useful in AMD and DR in predicting progression of these diseases.

A Narrowband 635 nm Autofluorescence Peak in Albino Mouse Eyes Found With Multi-Modal Imaging Reveals the Presence of Protoporphyrin IX in the Choroid

Purpose

To investigate differences in fundus autofluorescence (AF) spectra of pigmented (C57Bl/6) and albino (Balb/c) mouse retinas.

Methods

AF spectra were measured with a scanning laser ophthalmoscope (SLO) with a high-resolution spectrometer. The action spectrum of a 635 nm AF "spike" in albino mice was measured to estimate the underlying absorption spectrum. Optical coherence tomography (OCT) and OCT angiography were used to determine the most likely depth location of the source of the AF feature.

Results

Two narrowband emission peaks centered at 635 nm and ∼705 nm were observed in the AF spectra of albino (but not pigmented) mouse eyes. The dual-peak emission spectrum of the albino eye was extracted by subtracting a broadband emission that was similar in pigmented and albino mice: the two peaks correspond to emission spectra peaks of protoporphyrin IX, an obligate precursor in the biosynthesis of heme and cytochrome c. The action spectrum of the prominent 635 nm emission component corresponded with the PPIX absorption spectrum. The spatial distribution of the 635 nm emission did not correspond to that of the retinal vasculature but had a pattern more consistent with a choroidal origin.

Conclusions

Our results reveal that substantial PPIX is present in the posterior tissues of albino (Balb/c) mouse eyes, and measurable with high-resolution spectral measurements, and suggest that the absence of the narrow band PPIX emission peaks in pigmented eyes arises at least in part from screening by RPE and choroidal melanin.

Spectral-domain OCT characteristics of intraretinal hyper-reflective foci associated with age-related macular degeneration and diabetic retinopathy

OBJECTIVE

The purpose of this study was to quantitatively analyze and compare OCT characteristics of intraretinal hyper-reflective foci (IHRF) in eyes with diabetic retinopathy (DR) versus age-related macular degeneration (AMD).

DESIGN

a retrospective observational study.

PARTICIPANTS

54 treatment-naïve eyes (27 DR and 27 AMD).

METHODS

The IHRF lesions in OCT B-scan were semi-automatically segmented. Mean reflectivity (MR), maximum diameter, circularity index (Cir), area, and the angle between the greatest linear dimension (GLD) and the horizontal were computed for each IHRF lesion. The presence and absence of a posterior shadow and the axial location were assessed. The MR was normalized using the vitreous and nerve fiber layer reflectance as dark and bright reference standards, respectively.

RESULTS

A total of 1149 IHRF (1051 in DR and 98 in the AMD group) were identified, with a mean of 39 ± 36 lesions in DR eyes compared to only 4 ± 4 in AMD eyes (p < 0.001). The mean area of individual IHRF lesions was greater in DR eyes (1305 ± 1647 μm² vs 1031 ± 750 μm²; p = 0.016), but IHRF in AMD eyes had higher reflectivity (1.17 ± 0.14 vs 1.03 ± 0.17; p < 0.001). The angle of the GLD relative to the horizontal was greater in AMD eyes, indicating that IHRF in AMD eyes were more horizontally oriented. In AMD eyes, 88.8% of IHRF were located beneath the inner border of the outer nuclear layer (ONL), while in DR eyes, 56.9% were located there (p < 0.001).

CONCLUSIONS

IHRF lesions in eyes with DR and AMD demonstrate significant differences, with IHRF in DR eyes tending to be larger and less hyper-reflective compared to AMD eyes.

Quantifications of Outer Retinal Bands in Geographic Atrophy by Comparing Superior Axial Resolution and Conventional OCT

Purpose

Novel treatments for geographic atrophy (GA) require precise monitoring, which can be improved with advances in optical coherence tomography (OCT) technology. The purpose of this study was to investigate the benefits of a novel device with superior axial resolution.

Methods

Patients were recruited at the Department of Ophthalmology and Optometry at the Medical University of Vienna. Patients with GA were imaged with a Heidelberg SPECTRALIS HRA+OCT and the novel Heidelberg High-Res OCT device. Outer retinal bands and subretinal drusenoid deposits (SDDs) were segmented in 49 B-scans per OCT. Thickness and loss of outer retinal bands, as well as SDD volumes, were compared between devices and regions using linear mixed-effects models.

Results

The study included 3920 B-scans of 40 eyes of 32 patients. For the High-Res OCT, the myoid zone was thinner (19.85 µm, 95% confidence interval [CI] 16.8-22.8 vs. 21.37 µm, 95% CI 18.4-24.4; P < 0.001), whereas the ellipsoid zone (EZ) band was thicker (28.35 µm; 95% CI 22.7-24.0 vs. 27.29 µm, 95% CI 21.6-33.0). Smaller EZ- and external limiting membrane loss areas (all P < 0.001) were found for the High-Res OCT. The RPE band was thinner for the High-Res OCT (15.97 µm, 95% CI 13.5-18.4 vs. 21.08 µm, 95% CI 18.6-23.5; P < 0.001) without significant differences in RPE loss. Higher SDD volumes were found for the High-Res OCT (P < 0.001).

Conclusions

Precise in vivo quantification of OCT features is of great relevance for individualized patient management. The High-Res OCT device allows for detailed topographical analysis of outer retinal changes in GA, which could improve early detection, patient selection, and patient management in clinical practice.

Multidimensional Directionality-Enhanced Segmentation via large vision model

Optical Coherence Tomography (OCT) facilitates a comprehensive examination of macular edema and associated lesions. Manual delineation of retinal fluid is labor-intensive and error-prone, necessitating an automated diagnostic and therapeutic planning mechanism. Conventional supervised learning models are hindered by dataset limitations, while Transformer-based large vision models exhibit challenges in medical image segmentation, particularly in detecting small, subtle lesions in OCT images. This paper introduces the Multidimensional Directionality-Enhanced Retinal Fluid Segmentation framework (MD-DERFS), which reduces the limitations inherent in conventional supervised models by adapting a transformer-based large vision model for macular edema segmentation. The proposed MD-DERFS introduces a Multi-Dimensional Feature Re-Encoder Unit (MFU) to augment the model's proficiency in recognizing specific textures and pathological features through directional prior extraction and an Edema Texture Mapping Unit (ETMU), a Cross-scale Directional Insight Network (CDIN) furnishes a holistic perspective spanning local to global details, mitigating the large vision model's deficiencies in capturing localized feature information. Additionally, the framework is augmented by a Harmonic Minutiae Segmentation Equilibrium loss (LHMSE) that can address the challenges of data imbalance and annotation scarcity in macular edema datasets. Empirical validation on the MacuScan-8k dataset shows that MD-DERFS surpasses existing segmentation methodologies, demonstrating its efficacy in adapting large vision models for boundary-sensitive medical imaging tasks. The code is publicly available at https://github.com/IMOP-lab/MD-DERFS-Pytorch.git.

Comprehensive Evaluation of Human Donor Liver Viability with Polarization-Sensitive Optical Coherence Tomography

Human liver transplantation is severely constrained by a critical shortage of donor livers, with approximately one quarter of patients on the waiting list dying due to the scarcity of viable organs. Current liver viability assessments, which rely on invasive pathological methods, are hampered by limited sampling from biopsies, particularly in marginal livers from extended criteria donors (ECD) intended to expand the donor pool. Consequently, there is a pressing need for more comprehensive and non-invasive evaluation techniques to meet the escalating demand for liver transplants. In this study, we propose the use of polarization-sensitive optical coherence tomography (PS-OCT) to perform a thorough viability evaluation across the entire surface of donor livers. PS-OCT imaging was conducted on multiple regions, achieving near-complete coverage of the liver surface, and the findings were cross-validated with histopathological evaluations. The analysis of hepatic parameters derived from pathology highlighted tissue heterogeneity. Leveraging machine learning and texture analysis, we quantified hepatic steatosis, fibrosis, inflammation, and necrosis, and established strong correlations (≥ 80%) between PS-OCT quantifications and pathological assessments. PS-OCT offers a non-invasive assessment of liver viability by quantifying hepatic parenchymal parameters across the entire donor liver, significantly complementing current pathological analysis. These results suggest that PS-OCT provides a robust, non-invasive approach to assessing donor liver viability, which could potentially decrease the discard rate of higher risk livers, thereby expanding the donor pool and reducing the inadvertent use of those livers unsuitable for transplantation.

Expert consensus statement for quantitative measurement and morphologic assessment of optical coherence tomography: update 2025

In this updated expert consensus document, the methods for the quantitative measurement and morphologic assessment of optical coherence tomography (OCT) / optical frequency domain imaging images (OFDI) are briefly summarized. The focus is on the clinical application and the clinical evidence of OCT / OFDI to guide percutaneous coronary interventions.

Assessing diagnostic accuracy and monitoring of caries progression using optical coherence tomography (OCT): A systematic review

OBJECTIVES

This systematic review analyzed studies concerning the effectiveness of Optical Coherence Tomography (OCT) in detecting and monitoring dental caries in enamel and dentin.

DATA

Studies comparing the diagnostic accuracy of OCT in diagnosing caries clinically or using extracted teeth were included.

SOURCES

Databases, including PubMed/MEDLINE, Web of Science, EMBASE, and Scopus were searched for clinical trials and studies conducted using human teeth between 2010 and 2024. The process of study selection and data extraction followed the PRISMA guidelines. The methodological quality of the included studies was evaluated using the Critical Appraisal Skills Programme (CASP) diagnostic study checklist.

STUDY SELECTION

Seven studies met the inclusion criteria out of 1,266 articles identified. OCT modalities used in these studies include Cross-polarization OCT (CP-OCT), Swept-source OCT (SS-OCT), and polarization-sensitive (PS-OCT), with wavelengths ranging between 1310 and 1330 nm; axial resolutions were between 7.5-22 μm. SS-OCT had a high sensitivity of 74.1 % and specificity of 95.7 % when applied to caries detection. CP-OCT was able to identify dentin-penetrating lesions with greater accuracy than conventional radiography. There were strong correlations in the OCT measurements with the established diagnostic gold standards: polarized light microscopy (PLM), r = 0.63, P < 0.01; transverse microradiography (TMR), r = 0.75, P < 0.001.

CONCLUSION

OCT has demonstrated better performance regarding early caries detection and its activity compared with traditional diagnostic methods. However, standardization of the imaging protocol and further larger clinical trials are required.

CLINICAL SIGNIFICANCE

OCT offers significant clinical advantages in the diagnosis of dental caries. This technology enables early detection of carious lesions, facilitating timely intervention and potentially preserving tooth integrity. Investigating the clinical effectiveness of OCT is crucial to validate its utility in dental practice and to establish standardized protocols for its implementation in caries diagnosis.

Novel Application of Non-Invasive Methodological Approaches in Biomedical Sciences Towards Better Understanding of Marine Teleost Ocular Health and Disease

Seafood is an important resource for global nutrition and food security, with both land and marine aquaculture playing pivotal roles. High visual acuity is key for health and survival of farmed, cultured, and wild fish. Cleaner fish technology to control parasite infestation has become important in marine aquaculture and highlights the importance of visual acuity in the efficacy of cleaner fish species. New clinical diagnostic approaches towards understanding and optimising fish visual health could benefit both aquacultured and wild fish populations. Opportunities for developing and using advanced non-invasive clinical assessment and diagnosis of ocular health in wild, cultured, and experimental fish are key to more rapidly realising how threats to eye health in these animals might be better understood and mitigated. Ophthalmoscopy can rapidly and non-invasively image anatomical aspects of retinal and anterior ocular tissues and has been used in mammalian biomedicine since the turn of the 20th century. More now than ever, labour-intensive post-mortem approaches for ocular analysis such as histology are increasingly being replaced or supplemented by application of various forms of optical coherence tomography (OCT) imaging of ocular tissues in mammalian biomedicine. Advances and availability of other methodological approaches such as three-dimensional printing and computer science make instrument customisation affordable and adaptable. This review article will outline how ophthalmoscopy, OCT, and other methodologies are being applied towards understanding ocular health in teleost fish species and will describe some of the future opportunities that technological advances might afford in advancing ocular imaging in fish health and disease in general.

Characterization of Directional Reflectance in Corneal Tissue: A Comprehensive Optical Coherence Tomography Analysis

Purpose

To characterize the directional reflectance properties of the cornea using optical coherence tomography (OCT) imaging and develop a mathematical model describing corneal reflectance as a function of depth and incidence angle across different corneal layers.

Methods

A retrospective analysis was conducted on OCT scans from normal subjects using the Visionix Avanti OCT system (840 nm). Reflectance values for the epithelium, Bowman's layer, stroma, and endothelium/Descemet's membrane were extracted and analyzed as functions of incidence angle and corneal depth. Reflectance distributions were assessed for normality. Exponential functions were fitted to the mean and 97th percentile reflectance data to model directional reflectance for each corneal layer.

Results

Reflectance values exhibited non-normal leptokurtic distributions with right-tailed skewness, requiring non-parametric methods for percentile calculations. The exponential model incorporating angular dependence achieved R² values of 0.987 and 0.963 for mean and 97th percentile reflectance, respectively. The mean reflectance of the epithelium was modeled by a single exponential function, with half-reflectance angles of 15.9° to 26.6°. The stromal layers required two exponential components, with the anterior stroma exhibiting the highest reflectance and most pronounced directionality (half-reflectance angle of 0.17°). The 97th percentile reflectance differed, with higher reflectance values in the middle and posterior stroma. No statistically significant age or gender related variability in reflectance was measured.

Conclusions

This study provides a detailed mathematical model of corneal directional reflectance, highlighting the importance of incidence angle and layer depth in OCT image analysis.

Translational Relevance

The developed cornea reflectance model may improve OCT-based diagnostics by identifying early microstructural changes, aiding in the diagnosis and management of corneal diseases.

Exploring the impact of age and gender on retinal and choroidal thickness and vascular densities: a comprehensive analysis

PURPOSE

To evaluate the significance of age and gender on macular retinal and choroidal thicknesses (RT and CT) and related vascular density (VD) using optical coherence tomography angiography (OCTA) in a wide spectrum of normal participants.

METHODS

This was a cross-sectional study of 435 eyes of 234 normal healthy children and adults aged 5-97 years old who performed macular 3 × 3 mm scans using Optovue RTVue OCTA.

RESULTS

A statistically meaningful relationship was detected between age and macular layered VD and RT parameters. The middle retinal thickness (MRT) experiences minimal alterations throughout the lifespan. The mean foveal superficial capillary plexus density (SVD), deep capillary plexus density (DVD), and parafoveal SVD had the most density between the 20-40 years and tended to decrease to lowest point by the seventies. Foveal choriocapillaris vascular density (CVD), parafoveal CVD and parafoveal DVD had a decreasing course during the life course. There was no significant difference in choroidal vascular index between age groups. FAZ revealed relatively insignificant minor changes across age groups. The male participants had higher VD than the female participants in all measured parameters, except for parafoveal DVD.

CONCLUSION

RT of different layers as well as the whole retina is known to be influenced by age while the MRT experiences minimal alterations throughout the lifespan. The VD of male participants exceeded that of female participants across all measured parameters, except parafoveal DVD. Differential retinal and choroidal vascular plexuses have different course during a man's life.

'EarlyAMDRate': A grading instrument for OCT-based assessment of early lesions caused by age-related macular degeneration

BACKGROUND AND OBJECTIVES

Long before any signs of age-related macular degeneration (AMD) become clinically noticeable, the disease starts with accumulation of deposits of extracellular debris and formation of lesions within the outermost layers of the retina. For a reliable imaging of lesions in these early stages, optical coherence tomography (OCT) turned out to be largely preferable to colour fundus photography. However, an adequate grading instrument for Early-AMD lesions within OCT data is missing in the literature as yet. The present paper aims to fill this gap.

METHODS

'EarlyAMDRate', an instrument for OCT-based grading of Early-AMD lesions, is presented and documented. It comprises a questionnaire assessing a given lesion with respect to its relative position and interaction with the surrounding retinal layers, its brightness, special properties and state of progression (if applicable). Furthermore, the grading procedure includes a graphical masking of the lesion within the OCT image.

RESULTS

For a consecutive sample of N = 100 Early-AMD patients, the 'EarlyAMDRate' grading instrument has been applied to leading OCT scans. Examples of masked lesions and processed grading questionnaires are provided. Both raw lesion diameters and cutting sizes follow a log-normal sample distribution.

CONCLUSIONS

'EarlyAMDRate' allows for unprecedented detail of description for single Early-AMD lesions which is adequate to the precision of underlying OCT imaging. The obtained grading information allows for a tracking of single lesions and their properties over time as well as for the generation of well-differentiated metric phenotypes for description of Early-AMD.

Retinal Thickness Analysis Using Optical Coherence Tomography: Diagnostic and Monitoring Applications in Retinal Diseases

Retinal thickness analysis using optical coherence tomography (OCT) has become an indispensable tool in retinal disease management, providing high-resolution quantitative data for diagnosis, monitoring, and treatment planning. This analysis has been found to be particularly useful for both diagnostic and monitoring purposes across a wide range of retinal diseases, enabling precise disease characterization and treatment evaluation. This paper explores its applications across major retinal conditions, including age-related macular degeneration, diabetic retinopathy, retinal vein occlusion, and inherited retinal diseases. Emerging roles in other diseases such as neurodegenerative diseases and retinal drug toxicity are also highlighted. Despite challenges such as variability in measurements, segmentation errors, and interpretation difficulties, advancements in artificial intelligence and machine learning have significantly improved accuracy and efficiency. The integration of retinal thickness analysis with telemedicine platforms and standardized protocols further underscores its potential in delivering personalized care and enabling the early detection of ocular and systemic diseases. Retinal thickness analysis continues to play a pivotal and growing role in both clinical practice and research, bridging the gap between ophthalmology and broader medical fields.

Understanding gold nanoparticles and their attributes in ovarian cancer therapy

Ovarian cancer is one the deadliest disease wherein the survival rate is very low. Despite of advances in medical sciences, researches are still at the stage of infancy where patients are succumbing to this malignancy. Multidrug resistance, toxicity, mode of treatment related issues like catheter related complication poises a number of challenges to scientists worldwide. Novel therapy is now thus being focussed to sensitive the cells more towards the treatment. Gold nanoparticles (Au NPs), known for their high biocompatibility, and strong optical and magnetic responses, have emerged as promising agents for both the diagnosis and treatment of ovarian cancer. Owing to physical characteristics, AuNPs may be used as adjuvants in bioimaging, radiotherapy and fluorescence imaging. As a result, these characteristics substantially support AuNPs in biological domains. In addition to their therapeutic potential, Au NPs exhibit strong surface plasmon resonance (SPR) properties, enhancing imaging techniques for early detection of ovarian tumors. Furthermore, chemical properties such as Magnetic Resonance and Imaging Properties, X-ray imaging property, Two-photon or multiphoton imaging, and Optical coherence tomography (OCT) imaging properties enhance the use of Au NPs in diagnosis. This paper highlights the properties, targeting potential and diagnosis and treatment of ovarian cancer by Au NPs has been discussed.

Pupil wobble in point-scanning retinal optical coherence tomography systems

Optical coherence tomography (OCT) systems utilize 2D scanning methods to acquire reflectance-based volumetric images of samples, such as the human retina, with micrometer-scale depth resolution. A common method for performing this scanning at high speeds is to use a pair of sequential, single-axis galvanometer scanners. An undesired effect of using separated scanners is the variation in the beam position at the pupil plane, a phenomenon known as beam wander or pupil wobble. This can lead to loss of signal and vignetting artifacts in the resulting images. To overcome these limitations, we propose a method to deterministically analyze the pupil wobble in a given retinal OCT system and to correct for the displacement using pupil tracking OCT with a 2D scanning mirror placed anti-conjugate to the pupil plane. We demonstrate that we can model the pattern of pupil wobble present in any OCT system both theoretically and empirically and then use a pupil tracking system to correct for the displacement of the beam to acquire OCT images without the imposed artifacts.

Automated chick gender determination using optical coherence tomography and deep learning

Chick gender classification is crucial for optimizing poultry production, yet traditional methods such as vent sexing and ultrasound remain limited by human expertise, labor intensity, and insufficient resolution. This study introduces a novel approach that integrates Optical Coherence Tomography (OCT) and deep learning to enable high-resolution, non-invasive chick sexing. Unlike conventional imaging techniques, OCT provides micrometer-scale visualization of cloacal structures, allowing precise differentiation between male and female chicks based on internal anatomical markers. We developed a custom convolutional neural network (CNN) optimized for OCT data, incorporating asymmetric image resizing and enhanced feature extraction to improve classification accuracy. Our model achieved 79 % accuracy, outperforming conventional architectures such as Inception (63 %) and VGG-16 (74 %), highlighting the importance of a tailored, domain-specific model. This is the first study to integrate OCT with deep learning for automated chick sexing, demonstrating a scalable, real-time alternative to expert-dependent vent sexing. With further advancements in imaging and machine learning, our approach has the potential to transform chick sexing in commercial hatcheries, reducing reliance on skilled labor while enhancing classification efficiency and precision.

Experimental measurements of particle deposition in the human nasal airway

Intranasal drug delivery is a promising non-invasive method for administering both local and systemic medications. While previous studies have extensively investigated the effects of particle size, airflow dynamics, and deposition locations on deposition efficiency, limited attention has been given to the thickness of deposited particles, which can significantly affect drug dissolution, absorption and therapeutic efficacy. This study evaluated the deposition behaviour of three lactose powders in a silicone nasal airway replica under varying flow rates (15, 35, and 55 L/min) using optical coherence tomography (OCT). The main conclusion of these findings is that the anterior region of the nasal airway is the most effective site for capturing particles, exhibiting the highest deposition thickness and particle number density across all conditions. Specifically, deposition thickness exceeded 150 µm in some anterior regions, particularly under high flow rates, reaching up to 230 µm at 55 L/min for the most cohesive particle type (ML001). At 55 L/min, more cohesive particles, such as ML001, formed thicker clusters with deposition thickness 15-24 % greater than less cohesive particles like SV003 and SV010. Larger particles (SV010, D50 = 109 µm) mainly deposited in the anterior region, while smaller particles (SV003, D50 = 61 µm) showed a more uniform distribution, with deposition at location 1 about 10 % thicker than at location 2. Localised flow patterns, including recirculation zones, were identified as critical contributors to particle accumulation, as demonstrated by complementary computational fluid dynamics (CFD) simulations.

Diagnosis and Post-Treatment Follow-Up Evaluation of Melasma Using Optical Coherence Tomography and Deep Learning

Melasma is a common pigmentary disorder accompanied by tissue changes in composition and structure through the epidermis and dermis. In this study, we propose to employ optical coherence tomography (OCT) combined with deep learning techniques for melasma diagnostics. Specifically, a portable spectral domain OCT system with a handheld probe was developed for clinical skin imaging. Then, a diagnostic model was built based on the VGG16 neural network by adding a spatial attention mechanism. The results show that a good differentiation with an accuracy of 94.2% can be achieved among health datasets from healthy volunteers, and melasma and tissue-around-melasma datasets from melasma patients. Moreover, the same trained model was applied to treatment evaluation, showing a good capability to assess antivascular medicine treatment. Thus, it can be concluded that OCT combined with deep learning techniques has a good potential to aid in clinical diagnosis and treatment evaluation of melasma.

Gold Nanocages with a Long SPR Peak Wavelength as Contrast Agents for Optical Coherence Tomography Imaging at 1060 nm

There has been growing interest in optical coherence tomography (OCT) imaging at a wavelength of 1060 nm. However, potential contrast agents for OCT imaging at this specific wavelength has not been thoroughly investigated. In this study, we present the synthesis and optical characterization of gold nanocages with a small edge length (~65 nm) and a surface plasmon resonance peak around 1060 nm. These nanocages represent a class of potential contrast agents for OCT at 1060 nm. OCT imaging experiments were conducted on phantoms and in vivo mouse tissues using a 1060-nm swept-source OCT system, demonstrating significant enhancement in imaging contrast due to the presence of the gold nanocages.

Retinal thickness: A window into cognitive impairment in bipolar disorder

INTRODUCTION

Cognitive impairment (CI) in bipolar disorder (BD) significantly impacts overall functioning and quality of life. A better understanding of the neurobiological mechanisms associated with CI is needed. Studies on neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases, have revealed promising findings related to retinal thickness alterations using optical coherence tomography (OCT). Similarly, retinal differences between healthy controls and individuals with BD or schizophrenia have been described. This study explores the utility of OCT in discerning retinal changes possibly associated with CI in BD to enhance our understanding of the biological markers of BD and provide additional information to neuropsychological testing.

MATERIAL AND METHODS

Optical coherence tomography (OCT) was employed to measure retinal thickness in the macular ganglion cell layer (GCL), inner plexiform layer (IPL), retinal nerve fiber layer (RNFL), and peripapillary RNFL (pRNFL) in 50 individuals with bipolar disorder (BD). Associations with cognitive impairments were analyzed using cross-validated Random Forest models.

RESULTS

The analysis revealed significant associations between retinal thinning in various segments of the macular GCL, IPL, and RNFL and cognitive impairment (CI) in BD, with particular relevance to executive function deficits (AUC>0.8).

CONCLUSIONS

Thinning of the GCL, IPL, and RNFL was significantly associated with worse cognitive performance in individuals with BD. Similar patterns have been observed in schizophrenia, highlighting an innovative and promising field for research and clinical application.

En face OCT: Breakthroughs in understanding the pathoanatomy of retinal disease and clinical applications

En face optical coherence tomography (OCT) is a practical and informative imaging modality to noninvasively visualize distinct retinal and choroidal layers by providing coronal images using boundary-specific segmentation. Ongoing research with this method is generating breakthroughs in the illustration of new perspectives of retinal disease. The clinical value of en face OCT as an advanced retinal imaging tool is growing steadily and it has unveiled many new insights into the pathoanatomy of retinal disorders. Moreover, this modality can capture various en face OCT biomarkers that correspond to different cell or tissue subtypes, which were previously only identified through histological or electron microscopy methods, underscoring the significance of this technique in providing valuable pathoanatomical information. In this comprehensive review, we will systematically summarize the en face OCT findings across a broad spectrum of retinal diseases, including disorders of the vitreoretinal interface and retinal vascular system (e.g. paracentral acute middle maculopathy or PAMM and diabetic retinopathy), in addition to the en face OCT features of other conditions such as age-related macular degeneration, pachychoroid disease spectrum, myopic degeneration, uveitis and inflammatory disorders, inherited retinal dystrophies, and drug toxicity. We will discuss and highlight the unique clinical and pathoanatomical findings uncovered with en face OCT of each these diseases mentioned above.

Oculomics: Current concepts and evidence

The eye provides novel insights into general health, as well as pathogenesis and development of systemic diseases. In the past decade, growing evidence has demonstrated that the eye's structure and function mirror multiple systemic health conditions, especially in cardiovascular diseases, neurodegenerative disorders, and kidney impairments. This has given rise to the field of oculomics-the application of ophthalmic biomarkers to understand mechanisms, detect and predict disease. The development of this field has been accelerated by three major advances: 1) the availability and widespread clinical adoption of high-resolution and non-invasive ophthalmic imaging ("hardware"); 2) the availability of large studies to interrogate associations ("big data"); 3) the development of novel analytical methods, including artificial intelligence (AI) ("software"). Oculomics offers an opportunity to enhance our understanding of the interplay between the eye and the body, while supporting development of innovative diagnostic, prognostic, and therapeutic tools. These advances have been further accelerated by developments in AI, coupled with large-scale linkage datasets linking ocular imaging data with systemic health data. Oculomics also enables the detection, screening, diagnosis, and monitoring of many systemic health conditions. Furthermore, oculomics with AI allows prediction of the risk of systemic diseases, enabling risk stratification, opening up new avenues for prevention or individualized risk prediction and prevention, facilitating personalized medicine. In this review, we summarise current concepts and evidence in the field of oculomics, highlighting the progress that has been made, remaining challenges, and the opportunities for future research.

Estimation of normal and glaucomatous optic nerve morphology from perfusion

OBJECTIVE

To estimate the optic nerve head morphology from its haemoglobin (Hb) distribution.

METHODS AND ANALYSIS

The optic disc of 189 normal eyes and 292 with confirmed and suspected glaucoma were analysed with Spectralis-OCT and the Laguna ONhE application using the Topcon NW400 fundus camera. Topographic Hb values were correlated with OCT tissue thicknesses from Bruch's membrane. The neuroretinal rim volume (RV), the cup volume (CV) and their relation to the globin distribution function (GDF) index of Laguna ONhE were analysed. The results were applied to 1 163 241 optic nerve images obtained in a glaucoma screening setting. Differences in segmentation and the presence of vessels without local nutritional function are the main limitations of the comparison.

RESULTS

The correlation between local Hb density and the tissue thicknesses from Bruch's membrane was R=0.953 (p<0.0001). RV could be estimated from pixel-to-pixel Hb values with an R=0.650 (p<0.0001) and from six Laguna ONhE indices with an R=0.786 (p<0.0001). CV could be estimated with an R=0.762 (p<0.0001). RV had a mean value of 0.396 mm3 (SD=0.187) for positive GDF values and usually below 0.210 mm3 for negative GDF. The distribution of RV values in the screening series was congruent with that found in the prospective series, with a higher value in large nerves.

CONCLUSIONS

The volume and shape of the optic nerve tissue are closely related to its perfusion and can be deduced from it. The relationship between the RV and GDF is curvilinear and suggests that perfusion measurement may bring the diagnosis forward in earlier stages.

Effectiveness of Digital Dispersion Compensation in OCT

Dispersion mismatch in optical coherence tomography (OCT) is typically addressed through either physical or digital compensation. In this study, we investigate the impact of dispersion on OCT detection sensitivity and compare the effectiveness of physical and digital compensation across varying degrees of dispersion mismatch. Our results demonstrate that while digital dispersion compensation can effectively restore detection sensitivity, its efficacy is constrained by the severity of the dispersion mismatch. Beyond a certain threshold, digital compensation fails to fully recover image information, leading to degradation in image quality.

From morphology to single-cell molecules: high-resolution 3D histology in biomedicine

High-resolution three-dimensional (3D) tissue analysis has emerged as a transformative innovation in the life sciences, providing detailed insights into the spatial organization and molecular composition of biological tissues. This review begins by tracing the historical milestones that have shaped the development of high-resolution 3D histology, highlighting key breakthroughs that have facilitated the advancement of current technologies. We then systematically categorize the various families of high-resolution 3D histology techniques, discussing their core principles, capabilities, and inherent limitations. These 3D histology techniques include microscopy imaging, tomographic approaches, single-cell and spatial omics, computational methods and 3D tissue reconstruction (e.g. 3D cultures and spheroids). Additionally, we explore a wide range of applications for single-cell 3D histology, demonstrating how single-cell and spatial technologies are being utilized in the fields such as oncology, cardiology, neuroscience, immunology, developmental biology and regenerative medicine. Despite the remarkable progress made in recent years, the field still faces significant challenges, including high barriers to entry, issues with data robustness, ambiguous best practices for experimental design, and a lack of standardization across methodologies. This review offers a thorough analysis of these challenges and presents recommendations to surmount them, with the overarching goal of nurturing ongoing innovation and broader integration of cellular 3D tissue analysis in both biology research and clinical practice.