Choroidalyzer: An Open-Source, End-to-End Pipeline for Choroidal Analysis in Optical Coherence Tomography.

Purpose: To develop Choroidalyzer, an open-source, end-to-end pipeline for segmenting the choroid region, vessels, and fovea, and deriving choroidal thickness, area, and vascular index. Methods: We used 5600 OCT B-scans (233 subjects, six systemic disease cohorts, three device types, two manufacturers). To generate region and vessel ground-truths, we used state-of-the-art automatic methods following manual correction of inaccurate segmentations, with foveal positions manually annotated. We trained a U-Net deep learning model to detect the region, vessels, and fovea to calculate choroid thickness, area, and vascular index in a fovea-centered region of interest. We analyzed segmentation agreement (AUC, Dice) and choroid metrics agreement (Pearson, Spearman, mean absolute error [MAE]) in internal and external test sets. We compared Choroidalyzer to two manual graders on a small subset of external test images and examined cases of high error. Results: Choroidalyzer took 0.299 seconds per image on a standard laptop and achieved excellent region (Dice: internal 0.9789, external 0.9749), very good vessel segmentation performance (Dice: internal 0.8817, external 0.8703), and excellent fovea location prediction (MAE: internal 3.9 pixels, external 3.4 pixels). For thickness, area, and vascular index, Pearson correlations were 0.9754, 0.9815, and 0.8285 (internal)/0.9831, 0.9779, 0.7948 (external), respectively (all P < 0.0001). Choroidalyzer's agreement with graders was comparable to the intergrader agreement across all metrics. Conclusions: Choroidalyzer is an open-source, end-to-end pipeline that accurately segments the choroid and reliably extracts thickness, area, and vascular index. Especially choroidal vessel segmentation is a difficult and subjective task, and fully automatic methods like Choroidalyzer could provide objectivity and standardization.

An Open-Source Deep Learning Algorithm for Efficient and Fully Automatic Analysis of the Choroid in Optical Coherence Tomography.

Purpose: To develop an open-source, fully automatic deep learning algorithm, DeepGPET, for choroid region segmentation in optical coherence tomography (OCT) data. Methods: We used a dataset of 715 OCT B-scans (82 subjects, 115 eyes) from three clinical studies related to systemic disease. Ground-truth segmentations were generated using a clinically validated, semiautomatic choroid segmentation method, Gaussian Process Edge Tracing (GPET). We finetuned a U-Net with the MobileNetV3 backbone pretrained on ImageNet. Standard segmentation agreement metrics, as well as derived measures of choroidal thickness and area, were used to evaluate DeepGPET, alongside qualitative evaluation from a clinical ophthalmologist. Results: DeepGPET achieved excellent agreement with GPET on data from three clinical studies (AUC = 0.9994, Dice = 0.9664; Pearson correlation = 0.8908 for choroidal thickness and 0.9082 for choroidal area), while reducing the mean processing time per image on a standard laptop CPU from 34.49 ± 15.09 seconds using GPET to 1.25 ± 0.10 seconds using DeepGPET. Both methods performed similarly according to a clinical ophthalmologist who qualitatively judged a subset of segmentations by GPET and DeepGPET, based on smoothness and accuracy of segmentations. Conclusions: DeepGPET, a fully automatic, open-source algorithm for choroidal segmentation, will enable researchers to efficiently extract choroidal measurements, even for large datasets. As no manual interventions are required, DeepGPET is less subjective than semiautomatic methods and could be deployed in clinical practice without requiring a trained operator. Translational Relevance: DeepGPET addresses the lack of open-source, fully automatic, and clinically relevant choroid segmentation algorithms, and its subsequent public release will facilitate future choroidal research in both ophthalmology and wider systemic health.

Evaluation of an Automated Choroid Segmentation Algorithm in a Longitudinal Kidney Donor and Recipient Cohort.

Purpose: To evaluate the performance of an automated choroid segmentation algorithm in optical coherence tomography (OCT) data using a longitudinal kidney donor and recipient cohort. Methods: We assessed 22 donors and 23 patients requiring renal transplantation over up to 1 year posttransplant. We measured choroidal thickness (CT) and area and compared our automated CT measurements to manual ones at the same locations. We estimated associations between choroidal measurements and markers of renal function (estimated glomerular filtration rate [eGFR], serum creatinine, and urea) using correlation and linear mixed-effects (LME) modeling. Results: There was good agreement between manual and automated CT. Automated measures were more precise because of smaller measurement error over time. External adjudication of major discrepancies was in favor of automated measures. Significant differences were observed in the choroid pre- and posttransplant in both cohorts, and LME modeling revealed significant linear associations observed between choroidal measures and renal function in recipients. Significant associations were mostly stronger with automated CT (eGFR, P < 0.001; creatinine, P = 0.004; urea, P = 0.04) compared to manual CT (eGFR, P = 0.002; creatinine, P = 0.01; urea, P = 0.03). Conclusions: Our automated approach has greater precision than human-performed manual measurements, which may explain stronger associations with renal function compared to manual measurements. To improve detection of meaningful associations with clinical endpoints in longitudinal studies of OCT, reducing measurement error should be a priority, and automated measurements help achieve this. Translational Relevance: We introduce a novel choroid segmentation algorithm that can replace manual grading for studying the choroid in renal disease and other clinical conditions.

Edge Tracing Using Gaussian Process Regression.

We introduce a novel edge tracing algorithm using Gaussian process regression. Our edge-based segmentation algorithm models an edge of interest using Gaussian process regression and iteratively searches the image for edge pixels in a recursive Bayesian scheme. This procedure combines local edge information from the image gradient and global structural information from posterior curves, sampled from the model's posterior predictive distribution, to sequentially build and refine an observation set of edge pixels. This accumulation of pixels converges the distribution to the edge of interest. Hyperparameters can be tuned by the user at initialisation and optimised given the refined observation set. This tunable approach does not require any prior training and is not restricted to any particular type of imaging domain. Due to the model's uncertainty quantification, the algorithm is robust to artefacts and occlusions which degrade the quality and continuity of edges in images. Our approach also has the ability to efficiently trace edges in image sequences by using previous-image edge traces as a priori information for consecutive images. Various applications to medical imaging and satellite imaging are used to validate the technique and comparisons are made with two commonly used edge tracing algorithms.

AMPK-SIRT1-independent inhibition of ANGPTL3 gene expression is a potential lipid-lowering mechanism of metformin.

OBJECTIVES: Hypertriglyceridaemia enhances cardiovascular disease risk in patients with diabetes. Lipoprotein lipase (LPL) regulates plasma triglyceride levels by hydrolysing chylomicrons and very-low-density lipoprotein (VLDL). Metformin, an antidiabetic drug, improves plasma lipids including triglycerides. We examined metformin's regulation of angiopoietin-like 3 (ANGPTL3), a liver-derived secretory protein with LPL inhibitory property. METHODS: Using HepG2 cells, a human hepatocyte cell line, the effects of metformin on ANGPTL3 gene and protein expression were determined. The role of AMPK-SIRT1 pathway in metformin regulation of ANGPTL3 was determined using pharmacological, RNAi and reporter assays. Metformin regulation of ANGPTL3 expression was also examined in sodium palmitate-induced insulin resistance. KEY FINDINGS: Metformin and pharmacological activators of AMPK and SIRT1 inhibited the expression of ANGPTL3 in HepG2 cells. Pharmacological or RNAi-based antagonism of AMPK or SIRT1 failed to affect metformin inhibition of ANGPTL3. AMPK-SIRT1 activators and metformin exhibited distinct effects on the expression of ANGPTL3 gene luciferase reporter. Sodium palmitate-induced insulin resistance in cells resulted in increased ANGPTL3 gene expression which was suppressed by pretreatment with metformin. CONCLUSIONS: Metformin inhibits ANGPTL3 expression in the liver in an AMPK-SIRT1-independent manner as a potential mechanism to regulate LPL and lower plasma lipids.

Anti-edema action of thyroid hormone in MCAO model of ischemic brain stroke: Possible association with AQP4 modulation.

The use of neuroprotective strategies to mitigate the fatal consequences of ischemic brain stroke is a focus of robust research activity. We have previously demonstrated that thyroid hormone (T3; 3,3',5-triiodo-l-thyronine) possesses neuroprotective and anti-edema activity in pre-stroke treatment regimens when administered as a solution or as a nanoparticle formulation. In this study we have extended our evaluation of thyroid hormone use in animal models of brain stroke. We have used both transient middle cerebral artery occlusion (t-MCAO) and permanent (p-MCAO) models of ischemic brain stroke. A significant reduction of tissue infarction and a concurrent decrease in edema were observed in the t-MCAO model of brain stroke. However, no benefit of T3 was observed in p-MCAO stroke setting. Significant improvement of neurological outcomes was observed upon T3 treatment in t-MCAO mice. Further, we tested T2 (3,5-diiodo-l-thyronine) a natural deiodination metabolite of T3 in MCAO model of brain stroke. T2 potently decreased infarct size as well as edema formation. Additionally, we report here that T3 suppresses the expression of aquaporin-4 (AQP4) water channels which could be a likely mechanism of its anti-edema activity. Our studies provide evidence to stimulate clinical development of thyroid hormones for use in ischemic brain stroke.