ArticleiScience2022
SHAPR predicts 3D cell shapes from 2D microscopic images.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Cracking the Code: Computational Image Analysis Tools for Histopathological and Morphometric Insights.Journal of imaging · 2026Review
- A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis.Frontiers in medicine · 2025Review
- Diffusion models in bioinformatics and computational biology.Nature reviews bioengineering · 2024Article
- Automated MRI Lung Segmentation and 3D Morphologic Features for Quantification of Neonatal Lung Disease.Radiology. Artificial intelligence · 2023Article
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Authors and funding
6 authors.
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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 naïve stereological models and significantly increases the feature-based prediction of red blood cell types from F1 = 79% 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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Registered trials
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