Quantifying discordance between structure and function measurements in the clinical assessment of glaucoma

Objective: The visual field (VF) may be predicted from retinal nerve fibre layer thickness (RNFLT) using a Bayesian Radial Basis Function (BRBF). This study aimed to evaluate a new methodology to quantify and visualise discordance between structural and functional measurements in glaucomatous eyes. Methods: Five GDxVCC RNFLT scans and 5 Humphrey SITA VF tests were obtained from 50 glaucomatous eyes from 50 patients. A best available estimate of the ‘true’ VF was calculated as the point-wise median of these 5 replications. This ‘true’ VF was compared with every single RNFLT-predicted VF from BRBF and every single measured VF. Predictability of VFs from RNFLT was established from previous data. A structure-function pattern discordance map (PDM) and structure-function discordance index (SFDI; values 0 to 1) were established from the predictability limits for each structure-function measurement pair to quantify and visualise the discordance between the structure-predicted and measured VFs. Results: Mean absolute difference (MAD) between the structure-predicted and ‘true’ VFs was 3.9dB. MAD between single and ‘true’ VFs was 2.6dB. Mean of SFDI was 0.34 (SD 0.11). 39% of the structure-predicted VFs showed significant discordance (SFDI>0.3) from measured VFs. Conclusions: BRBF, on average, predicts the ‘true’ VF from RNFLT slightly less well than a measured VF from the 5 VFs compromising the ‘true’ VF. The PDM highlights locations with structure-function discordance, with the SFDI providing a summary index. These tools may help clinicians trust the mutually confirmatory structure-function measurements with good concordance, or identify unreliable ones with poor concordance

Astigmatism and Pseudoaccommodation in Pseudophakic Eyes

noAdvanced IOLs with circumferential zones of different power provide pseudoaccommodation. We investigated the potential for power variation with meridian, namely astigmatism, to provide pseudo-accommodation. With appropriate power and axis orientations, acceptable pseudo-accommodation can be achieved

Multidimensional Approach to Comparative Avian Visual Systems

Since the birth of visual ecology, comparative studies on how birds see their world have been limited to a small number of species and tended to focus on a single visual trait. This approach has constrained our ability to understand the diversity and evolution of the avian visual system. The goal of this dissertation was to characterize multiple visual dimensions on bird groups that are highly speciouse (e.g., Passeriformes), and test some hypotheses and predictions, using modern comparative tools, on the relationship between different visual traits and their association with visual information sampling behaviors. First, I developed a novel method for characterizing quantitatively the retinal topography (e.g., variation in cell density across the retina) of different bird species in a standardized manner. Second, using this method, I established that retinal configuration has converged particularly in terrestrial vertebrates into three types of retinal specializations: fovea, area, and visual streak, with the highest, intermediate, and lowest peak and peripheral ganglion cell densities, respectively. The implication is that foveate species may have more enhanced visual centers in the brain than non-foveate vertebrates. Third, forest passerines that form multi-species flocks and belong to an insectivore niche differ in their visual system configuration, which appeared associated to behavioral specializations to enhance foraging opportunities: species that searched for food at steep angles had relatively wide binocular fields with a high degree of eye movement right above their short bills, whereas species that searched for food at shallower angles had narrower binocular fields with a high degree of eye movement below their bills. Eye movement allows these species to move their fovea around to visually search for food in the complex forest environment. Fourth, I studied the visual system configuration of nine species of closely related emberizid sparrows, which appear to maximize binocular vision, even seeing their bill tips, to enhance food detection and handling. Additionally, species with more visual coverage had higher visual acuity, which may compensate for their larger blind spots above their foveae, enhancing predator detection. Overall, the visual configuration of these passive prey foragers is substantially different from previously studied avian groups (e.g., sit-and-wait and tactile foragers). Finally, I studied the visual system configuration and visual exploratory behavior of 29 North American bird species across 14 Families. I found that species with a wider blind spot in the visual field (pecten) tended to move their heads at a higher rate probably to compensate for the lack of visual information. Additionally, species with a more pronounced difference in cell density between the fovea and the retinal periphery tended to have a higher degree of eye movement likely to enhance their ability to move their fovea around to gather high quality information. Overall, the avian visual system seems to have specializations to enhance both foraging and anti-predator behaviors that differ greatly between species probably to adjust to specific environmental conditions

Single-breath-hold photoacoustic computed tomography of the breast

We have developed a single-breath-hold photoacoustic computed tomography (SBH-PACT) system to reveal detailed angiographic structures in human breasts. SBH-PACT features a deep penetration depth (4 cm in vivo) with high spatial and temporal resolutions (255 µm in-plane resolution and a 10 Hz 2D frame rate). By scanning the entire breast within a single breath hold (~15 s), a volumetric image can be acquired and subsequently reconstructed utilizing 3D back-projection with negligible breathing-induced motion artifacts. SBH-PACT clearly reveals tumors by observing higher blood vessel densities associated with tumors at high spatial resolution, showing early promise for high sensitivity in radiographically dense breasts. In addition to blood vessel imaging, the high imaging speed enables dynamic studies, such as photoacoustic elastography, which identifies tumors by showing less compliance. We imaged breast cancer patients with breast sizes ranging from B cup to DD cup, and skin pigmentations ranging from light to dark. SBH-PACT identified all the tumors without resorting to ionizing radiation or exogenous contrast, posing no health risks

Optical coherence tomography angiography of optic nerve head and parafovea in multiple sclerosis

Aims To investigate swept-source optical coherence tomography (OCT) angiography in the optic nerve head (ONH) and parafoveal regions in patients with multiple sclerosis (MS). Methods Fifty-two MS eyes and 21 healthy control (HC) eyes were included. There were two MS subgroups: 38 MS eyes without an optic neuritis (ON) history (MS −ON), and 14 MS eyes with an ON history (MS +ON). The OCT images were captured by high-speed 1050 nm swept-source OCT. The ONH flow index (FI) and parafoveal FI were quantified from OCT angiograms. Results The mean ONH FI was 0.160±0.010 for the HC group, 0.156±0.017 for the MS−ON group, and 0.140±0.020 for the MS+ON group. The ONH FI of the MS+ON group was reduced by 12.5% compared to HC eyes (p=0.004). A higher percentage of MS+ON eyes had abnormal ONH FI compared to HC patients (43% vs 5%, p=0.01). Mean parafoveal FIs were 0.126±0.007, 0.127±0.010, and 0.129±0.005 for the HC, MS−ON, and MS +ON groups, respectively, and did not differ significantly among them. The coefficient of variation (CV) of intravisit repeatability and intervisit reproducibility were 1.03% and 4.53% for ONH FI, and 1.65% and 3.55% for parafoveal FI. Conclusions Based on OCT angiography, the FI measurement is feasible, highly repeatable and reproducible, and it is suitable for clinical measurement of ONH and parafoveal perfusion. The ONH FI may be useful in detecting damage from ON and quantifying its severity.National Institutes of Health (U.S.) (Clinical and Translational Science Award Grant UL1TR000128)National Institutes of Health (U.S.) (Grant R01 EY023285)National Institutes of Health (U.S.) (Grant R01 EY013516)National Institutes of Health (U.S.) (Grant R01-EY11289)National Institutes of Health (U.S.) (Grant P30EY010572)United States. Air Force Office of Scientific Research (FA9550-10-1-0551)United States. Air Force Office of Scientific Research (FA9550-12-1-0499)German Research Foundation (DFG-HO-1791/11-1)Research to Prevent Blindness, Inc. (United States

Fully automatic segmentation and monitoring of choriocapillaris flow voids in OCTA images

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG.[Abstract]: Optical coherence tomography angiography (OCTA) is a non-invasive ophthalmic imaging modality that is widely used in clinical practice. Recent technological advances in OCTA allow imaging of blood flow deeper than the retinal layers, at the level of the choriocapillaris (CC), where a granular image is obtained showing a pattern of bright areas, representing blood flow, and a pattern of small dark regions, called flow voids (FVs). Several clinical studies have reported a close correlation between abnormal FVs distribution and multiple diseases, so quantifying changes in FVs distribution in CC has become an area of interest for many clinicians. However, CC OCTA images present very complex features that make it difficult to correctly compare FVs during the monitoring of a patient. In this work, we propose fully automatic approaches for the segmentation and monitoring of FVs in CC OCTA images. First, a baseline approach, in which a fully automatic segmentation methodology based on local contrast enhancement and global thresholding is proposed to segment FVs and measure changes in their distribution in a straightforward manner. Second, a robust approach in which, prior to the use of our segmentation methodology, an unsupervised trained neural network is used to perform a deformable registration that aligns inconsistencies between images acquired at different time instants. The proposed approaches were tested with CC OCTA images collected during a clinical study on the response to photodynamic therapy in patients affected by chronic central serous chorioretinopathy (CSC), demonstrating their clinical utility. The results showed that both approaches are accurate and robust, surpassing the state of the art, therefore improving the efficacy of FVs as a biomarker to monitor the patient treatments. This gives great potential for the clinical use of our methods, with the possibility of extending their use to other pathologies or treatments associated with this type of imaging.Xunta de Galicia; ED481B-2021-059Xunta de Galicia; ED431C 2020/24Xunta de Galicia; IN845D 2020/38Xunta de Galicia; ED431G 2019/01This research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research projects with references PID2019-108435RB-I00; TED2021-131201B-I00 and PDC2022-133132-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the postdoctoral, grant ref. ED481B-2021-059; and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80 % through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, and the remaining 20 % by “Secretaría Xeral de Universidades”, grant ref. ED431G 2019/01. Emilio López Varela acknowledges its support under FPI Grant Program through PID2019-108435RB-I00 project. Funding for open access charge: Universidade da Coruña/CISUG

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Numerical Model for the Determination of Erythrocyte Mechanical Properties and Wall Shear Stress in vivo From Intravital Microscopy.

The mechanical properties and deformability of Red Blood Cells (RBCs) are important determinants of blood rheology and microvascular hemodynamics. The objective of this study is to quantify the mechanical properties and wall shear stress experienced by the RBC membrane during capillary plug flow in vivo utilizing high speed video recording from intravital microscopy, biomechanical modeling, and computational methods. Capillaries were imaged in the rat cremaster muscle pre- and post-RBC transfusion of stored RBCs for 2-weeks. RBC membrane contours were extracted utilizing image processing and parametrized. RBC parameterizations were used to determine updated deformation gradient and Lagrangian Green strain tensors for each point along the parametrization and for each frame during plug flow. The updated Lagrangian Green strain and Displacement Gradient tensors were numerically fit to the Navier-Lame equations along the parameterized boundary to determined Lame's constants. Mechanical properties and wall shear stress were determined before and transfusion, were grouped in three populations of erythrocytes: native cells (NC) or circulating cells before transfusion, and two distinct population of cells after transfusion with stored cells (SC1 and SC2). The distinction, between the heterogeneous populations of cells present after the transfusion, SC1 and SC2, was obtained through principle component analysis (PCA) of the mechanical properties along the membrane. Cells with the first two principle components within 3 standard deviations of the mean, were labeled as SC1, and those with the first two principle components greater than 3 standard deviations from the mean were labeled as SC2. The calculated shear modulus average was 1.1±0.2, 0.90±0.15, and 12 ± 8 MPa for NC, SC1, and SC2, respectively. The calculated young's modulus average was 3.3±0.6, 2.6±0.4, and 32±20 MPa for NC, SC1, and SC2, respectively. o our knowledge, the methods presented here are the first estimation of the erythrocyte mechanical properties and shear stress in vivo during capillary plug flow. In summary, the methods introduced in this study may provide a new avenue of investigation of erythrocyte mechanics in the context of hematologic conditions that adversely affect erythrocyte mechanical properties

Automatic detection and counting of retina cell nuclei using deep learning

The ability to automatically detect, classify, calculate the size, number, and grade of retinal cells and other biological objects is critically important in eye disease like age-related macular degeneration (AMD). In this paper, we developed an automated tool based on deep learning technique and Mask R-CNN model to analyze large datasets of transmission electron microscopy (TEM) images and quantify retinal cells with high speed and precision. We considered three categories for outer nuclear layer (ONL) cells: live, intermediate, and pyknotic. We trained the model using a dataset of 24 samples. We then optimized the hyper-parameters using another set of 6 samples. The results of this research, after applying to the test datasets, demonstrated that our method is highly accurate for automatically detecting, categorizing, and counting cell nuclei in the ONL of the retina. Performance of our model was tested using general metrics: general mean average precision (mAP) for detection; and precision, recall, F1-score, and accuracy for categorizing and counting.Comment: 13 pages, 11 figures, 2 tables, SPIE. Medical Imaging 2020 Conferenc