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We consider the problem of accurately identifying cell boundaries and labeling individual cells in confocal microscopy images, specifically, 3D image stacks of cells with tagged cell membranes. Precise identification of cell boundaries, their shapes, and quantifying inter-cellular space leads to a better understanding of cell morphogenesis. Towards this, we outline a cell segmentation method that uses a deep neural network architecture to extract a confidence map of cell boundaries, followed by a 3D watershed algorithm and a final refinement using a conditional random field. In addition to improving the accuracy of segmentation compared to other state-ofthe-art methods, the proposed approach also generalizes well to different datasets without the need to retrain the network for each dataset. Detailed experimental results are provided, and the source code is available on GitHub 1 .

The optical and electrical characteristics of InGaN blue and green micro-light-emitting diodes (μLEDs) with GaN tunnel junction (TJ) contacts grown by metalorganic chemical vapor deposition (MOCVD) were compared at different activation temperatures among three activation methods from the literature, namely, sidewall activation, selective area growth (SAG), and chemical treatment before sidewall activation. The devices with chemical treatment before activation resulted in uniform electroluminescence and higher light output power, compared to the devices with sidewall activation and SAG. Moreover, the green μLEDs showed greater optical degradation at elevated activation temperatures, whereas the blue μLEDs yielded trivial difference with activation temperatures from 670 to 790 °C. The 5 × 5 μm2 devices with chemical treatment before activation and SAG yielded almost identical voltage at 20 A/cm2, and the voltage penalty significantly decreased with activation temperature in the case of devices with sidewall activation. The devices with chemical treatment before activation resulted in higher external quantum efficiency (EQE) and wall-plug efficiency (WPE) in low current density range compared to the devices with SAG. The enhancements in EQE and WPE were observed in different μLED sizes, suggesting that chemical treatment before sidewall activation enables the use of TJ contacts grown by MOCVD and is advantageous for applications that require high brightness and efficiency.

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Purpose To examine deep learning (DL)–based methods for accurate segmentation of geographic atrophy (GA) lesions using fundus autofluorescence (FAF) and near-infrared (NIR) images. Methods This retrospective analysis utilized imaging data from study eyes of patients enrolled in Proxima A and B (NCT02479386; NCT02399072) natural history studies of GA. Two multimodal DL networks (UNet and YNet) were used to automatically segment GA lesions on FAF; segmentation accuracy was compared with annotations by experienced graders. The training data set comprised 940 image pairs (FAF and NIR) from 183 patients in Proxima B; the test data set comprised 497 image pairs from 154 patients in Proxima A. Dice coefficient scores, Bland–Altman plots, and Pearson correlation coefficient ( r ) were used to assess performance. Results On the test set, Dice scores for the DL network to grader comparison ranged from 0.89 to 0.92 for screening visit; Dice score between graders was 0.94. GA lesion area correlations ( r ) for YNet versus grader, UNet versus grader, and between graders were 0.981, 0.959, and 0.995, respectively. Longitudinal GA lesion area enlargement correlations ( r ) for screening to 12 months ( n = 53) were lower (0.741, 0.622, and 0.890, respectively) compared with the cross-sectional results at screening. Longitudinal correlations ( r ) from screening to 6 months ( n = 77) were even lower (0.294, 0.248, and 0.686, respectively). Conclusions Multimodal DL networks to segment GA lesions can produce accurate results comparable with expert graders. Translational Relevance DL-based tools may support efficient and individualized assessment of patients with GA in clinical research and practice.