TY - JOUR T1 - 3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification JF - bioRxiv DO - 10.1101/313411 SP - 313411 AU - Alexandr A. Kalinin AU - Ari Allyn-Feuer AU - Alex Ade AU - Gordon-Victor Fon AU - Walter Meixner AU - David Dilworth AU - Syed S. Husain 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 Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/05/16/313411.abstract N2 - Quantitative analysis of morphological changes in a cell nucleus is important for understanding of nuclear architecture and their relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes present challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wise analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We use robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we compute geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compare over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieves accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D. ER -