3D Cell Nuclear Morphology: Microscopy Imaging Dataset And Voxel-Based Morphometry Classification Results
By Alexandr A Kalinin, Ari Allyn-Feuer, A. Ade, Gordon-Victor Fon, W. Meixner, D. Dilworth, J. D. Wet, Gerald A. Higgins, Gen Zheng, A. Creekmore and 7 more. 2017
23 citations recorded by the source corpus.
Cell deformation is regulated by complex underlying biological mechanisms associated with spatial and temporal morphological changes in the nucleus. 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. Multiple 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. To address this problem, we present a dataset containing a of total of 1,433 segmented nuclear and 3,282 nucleolar binary masks. We also provide a baseline evaluation of a number of popular classification algorithms using voxel-based morphometric measures. Original and derived imaging data are made publicly available for downloading on the project web-page: http://www.socr.umich.edu/projects/3d-cell-morphometry/data.html.
Subfields: Artificial Intelligence, Biophysics, Media Technology
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Citations in the corpus
- Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis (2018, unknown): “.”
- Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, background): “underlying reorganization of the chromatin architecture in 3D [54].”