PT  - JOURNAL ARTICLE
AU  - Alexandr A. Kalinin
AU  - Ari Allyn-Feuer
AU  - Alex Ade
AU  - Gordon-Victor Fon
AU  - Walter Meixner
AU  - David Dilworth
AU  - Jeffrey R. de Wet
AU  - Gerald A. Higgins
AU  - Gen Zheng
AU  - Amy Creekmore
AU  - John W. Wiley
AU  - James E. Verdone
AU  - Robert W. Veltri
AU  - Kenneth J. Pienta
AU  - Donald S. Coffey
AU  - Brian D. Athey
AU  - Ivo D. Dinov
TI  - 3D Cell Nuclear Morphology: Microscopy Imaging Dataset and Voxel-Based Morphometry Classification Results
AID  - 10.1101/208207
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 208207
4099  - http://biorxiv.org/content/early/2018/04/22/208207.short
4100  - http://biorxiv.org/content/early/2018/04/22/208207.full
AB  - Cell deformation is regulated by complex underlying biological mechanisms associated with spatial and temporal morphological changes in the nucleus that are related to cell differentiation, development, proliferation, and disease. Thus, quantitative analysis of changes in size and shape of nuclear structures in 3D microscopic images is important not only for investigating nuclear organization, but also for detecting and treating pathological conditions such as cancer. While many efforts have been made to develop cell and nuclear shape characteristics in 2D or pseudo-3D, several studies have suggested that 3D morphometric measures provide better results for nuclear shape description and discrimination. A few methods have been proposed to classify cell and nuclear morphological phenotypes in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. This limitation becomes of great importance when the ability to evaluate different approaches on benchmark data is needed for better dissemination of the current state of the art methods for bioimage analysis. To address this problem, we present a dataset containing two different cell collections, including original 3D microscopic images of cell nuclei and nucleoli. In addition, we perform a baseline evaluation of a number of popular classification algorithms using 2D and 3D voxel-based morphometric measures. To account for batch effects, while enabling calculations of AUROC and AUPR performance metrics, we propose a specific cross-validation scheme that we compare with commonly used k-fold cross-validation. Original and derived imaging data are made publicly available on the project web-page: http://www.socr.umich.edu/projects/3d-cell-morphometry/data.html.