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bioRxiv · 10.1101/2021.09.29.462353

SHAPR - An AI approach to predict 3D cell shapes from 2D microscopic images

Waibel, D.·Kiermeyer, N.·Atwell, S.·Sadafi, A.·Meier, M.·Marr, C.
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Abstract

Reconstruction of shapes and sizes of three-dimensional (3D) objects from two-dimensional (2D) information is an intensely studied subject in computer vision. We here consider the level of single cells and nuclei and present a neural network-based SHApe PRediction autoencoder. For proof-of-concept, SHAPR reconstructs 3D shapes of red blood cells from single view 2D confocal microscopy images more accurately than naive stereological models and significantly increases the feature-based prediction of red blood cell types from F1 = 79.0% to F1 = 87.4%. Applied to 2D images containing spheroidal aggregates of densely grown human induced pluripotent stem cells, we find that SHAPR learns fundamental shape properties of cell nuclei and allows for prediction-based morphometry. Reducing imaging time and data storage, SHAPR will help to optimize and up-scale image-based high-throughput applications for biomedicine.

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Waibel, D., Kiermeyer, N., Atwell, S., Sadafi, A., Meier, M., Marr, C.. 2021-10-01. SHAPR - An AI approach to predict 3D cell shapes from 2D microscopic images. https://doi.org/10.1101/2021.09.29.462353

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