Reconstruction of shapes, forms, and sizes of three-dimensional (3D) objects from two-dimensional (2D) information is one of the most complex functions of the human brain. It also poses an algorithmic challenge and at present is a widely studied subject in computer vision. We here focus on the single cell level and present a neural network-based SHApe PRediction autoencoder SHAPR that accurately reconstructs 3D cellular and nuclear shapes from 2D microscopic images and may have great potential for application in the biomedical sciences.
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