Three-dimensional cell and tissue image analysis for cellular and sub-cellular morphological modeling and classification

Abstract

The ability to automate the processes of specimen collection, image acquisition, data pre-processing, computation of derived biomarkers, modeling, classification and analysis can significantly impactclinical decision-making and fundamental investigation of cell deformation. This disclosure combine 3D cell nuclear shape modeling by robust smooth surface reconstruction and extraction of shape morphometry measure into a highly parallel pipeline workflow protocol for end-to-end morphological analysis of thousands of nuclei and nucleoli in 3D. This approach allows efficient and informative evaluation of cell shapes in the imaging data and represents a reproducible technique that can be validated, modified, and repurposed by the biomedical community. This facilitates result reproducibility, collaborative method validation, and broad knowledge dissemination.

Description

[0001] government terms [0002] This invention was made with government support under NR015331, NS091856; DK089503 and EB020406 awarded by the National Institutes of Health, and IIS-636840 awarded by the National Science Foundation. The government has certain rights in this invention. [0003] Cross References to Related Applications [0004] This application claims priority to U.S. Patent Application No. 16 / 277,128, filed February 15, 2019, and also claims the benefit of U.S. Provisional Application No. 62 / 632,663, filed February 20, 2018. The entire disclosure of the above application is incorporated herein by reference. technical field [0005] The present disclosure relates to three-dimensional cell and tissue image analysis for modeling and classification of cellular and subcellular morphology. Background technique [0006] Tissue and cellular deformation is regulated by complex underlying biological mechanisms that affect spatial and temporal morphological changes....

AI Technical Summary

Problems solved by technology

However, the dimensionality and quality of imaging data and the great variability of cellular and subcellular shapes across populations pose challenges for 3D shape methods that should allow accurate morphological analysis, should be scalable , robust to noise and at the same time sufficiently specific across populations

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