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We examine shifted symplectic and Poisson structures on spaces of framed maps. We prove some results about shifted Poisson structures analogous to those in [PTVV] and [Ca] about symplectic structures. Then, we consider the space Map(X, D, Y ) of maps from X to Y framed along a divisor D. We give conditions under which this space has a shifted symplectic or Poisson structure. Classical examples of symplectic and Poisson structures are provided with this theorem.

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.