PurposeTo characterize quantitative optical coherence tomography angiography (OCT-A) parameters in active neovascular age-related macular degeneration (nAMD) patients under treatment and remission nAMD patients.DesignRetrospective, cross-sectional study.ParticipantsOne hundred and four patients of whom 72 were in Group 1 (active nAMD) and 32 in Group 2 (remission nAMD) based on SD-OCT (Spectral Domain OCT) qualitative morphology.MethodsThis study was conducted at the Centre Ophtalmologique de l’Odeon between June 2016 and December 2017. Eyes were analyzed using SD-OCT and high-speed (100 000 A-scans/second) 1050-nm wavelength swept-source OCT-A. Speckle noise removal and choroidal neovascularization (CNV) blood flow delineation were automatically performed. Quantitative parameters analyzed included blood flow area (Area), vessel density, fractal dimension (FD) and lacunarity. OCT-A image algorithms and graphical user interfaces were built as a unified tool in Matlab coding language. Generalized Additive Models were used to study the association between OCT-A parameters and nAMD remission on structural OCT. The models’ performance was assessed by the Akaike Information Criterion (AIC), Brier Score and by the area under the receiver operating characteristic curve (AUC). A p value of ≤ 0.05 was considered as statistically significant.ResultsArea, vessel density and FD were different (p<0.001) in the two groups. Regarding the association with CNV activity, Area alone had the highest AUC (AUC = 0.85; 95%CI: 0.77–0.93) followed by FD (AUC = 0.80; 95%CI: 0.71–0.88). Again, Area obtained the best values followed by FD in the AIC and Brier Score evaluations. The multivariate model that included both these variables attained the best performance considering all assessment criteria.ConclusionsBlood flow characteristics on OCT-A may be associated with exudative signs on structural OCT. In the future, analyses of OCT-A quantitative parameters could potentially help evaluate CNV activity status and to develop personalized treatment and follow-up cycles.

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The cell nucleus is a tightly regulated organelle and its architectural structure is dynamically orchestrated to maintain normal cell function. Indeed, fluctuations in nuclear size and shape are known to occur during the cell cycle and alterations in nuclear morphology are also hallmarks of many diseases including cancer. Regrettably, automated reliable tools for cell cycle staging at single cell level using in situ images are still limited. It is therefore urgent to establish accurate strategies combining bioimaging with high-content image analysis for a bona fide classification. In this study we developed a supervised machine learning method for interphase cell cycle staging of individual adherent cells using in situ fluorescence images of nuclei stained with DAPI. A Support Vector Machine (SVM) classifier operated over normalized nuclear features using more than 3500 DAPI stained nuclei. Molecular ground truth labels were obtained by automatic image processing using fluorescent ubiquitination-based cell cycle indicator (Fucci) technology. An average F1-Score of 87.7% was achieved with this framework. Furthermore, the method was validated on distinct cell types reaching recall values higher than 89%. Our method is a robust approach to identify cells in G1 or S/G2 at the individual level, with implications in research and clinical applications.

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Butterflies are increasingly becoming model insects where basic questions surrounding the diversity of their color patterns are being investigated. Some of these color patterns consist of simple spots and eyespots. To accelerate the pace of research surrounding these discrete and circular pattern elements we trained distinct convolutional neural networks (CNNs) for detection and measurement of butterfly spots and eyespots on digital images of butterfly wings. We compared the automatically detected and segmented spot/eyespot areas with those manually annotated. These methods were able to identify and distinguish marginal eyespots from spots, as well as distinguish these patterns from less symmetrical patches of color. In addition, the measurements of an eyespot’s central area and surrounding rings were comparable with the manual measurements. These CNNs offer improvements of eyespot/spot detection and measurements relative to previous methods because it is not necessary to mathematically define the feature of interest. All that is needed is to point out the images that have those features to train the CNN.