3D cell nuclear morphology: Microscopy imaging dataset and voxel-based morphometry classification results

Alexandr A. Kalinin, Ari Allyn-Feuer, Alex Ade, Gordon Victor Fon, Walter Meixner, David Dilworth, Jeffrey R. De Wet, Gerald A. Higgins, Gen Zheng, Amy Creekmore, John W. Wiley, James E. Verdone, Robert W. Veltri, Kenneth J. Pienta, Donald S Coffey, Brian D. Athey, Ivo D. Dinov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 AU-ROC 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 webpage: http://www.socr.umich.edu/projects/3d-cell-morphometry/data.html.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
PublisherIEEE Computer Society
Pages2353-2361
Number of pages9
Volume2018-June
ISBN (Electronic)9781538661000
DOIs
StatePublished - Dec 13 2018
Event31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 - Salt Lake City, United States
Duration: Jun 18 2018Jun 22 2018

Other

Other31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
CountryUnited States
CitySalt Lake City
Period6/18/186/22/18

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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  • Cite this

    Kalinin, A. A., Allyn-Feuer, A., Ade, A., Fon, G. V., Meixner, W., Dilworth, D., De Wet, J. R., Higgins, G. A., Zheng, G., Creekmore, A., Wiley, J. W., Verdone, J. E., Veltri, R. W., Pienta, K. J., Coffey, D. S., Athey, B. D., & Dinov, I. D. (2018). 3D cell nuclear morphology: Microscopy imaging dataset and voxel-based morphometry classification results. In Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 (Vol. 2018-June, pp. 2353-2361). [8575476] IEEE Computer Society. https://doi.org/10.1109/CVPRW.2018.00304