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 impact clinical 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.

Abstract: A method is presented for generating a data set from a database. The method involves iterative data manipulation that stochastically identifies candidate entries from the cases (subjects, participants) and variables (data elements) and subsequently selects, nullifies, and imputes the information. This process heavily relies on statistical multivariate imputation to preserve the joint distributions of the complex structured data archive. At each step, the algorithm generates a complete dataset that in aggregate closely resembles the intrinsic characteristics of the original data set, however, on an individual level the rows of data are substantially altered. This procedure drastically reduces the risk for subject reidentification by stratification, as meta-data for all subjects is repeatedly and lossily encoded.

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 impact clinical 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.

Abstract: A method is presented for generating a data set from a database. The method involves iterative data manipulation that stochastically identifies candidate entries from the cases (subjects, participants) and variables (data elements) and subsequently selects, nullifies, and imputes the information. This process heavily relies on statistical multivariate imputation to preserve the joint distributions of the complex structured data archive. At each step, the algorithm generates a complete dataset that in aggregate closely resembles the intrinsic characteristics of the original data set, however, on an individual level the rows of data are substantially altered. This procedure drastically reduces the risk for subject reidentification by stratification, as meta-data for all subjects is repeatedly and lossily encoded.