TY - GEN
T1 - 3D cell nuclear morphology
T2 - 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
AU - Kalinin, Alexandr A.
AU - Allyn-Feuer, Ari
AU - Ade, Alex
AU - Fon, Gordon Victor
AU - Meixner, Walter
AU - Dilworth, David
AU - De Wet, Jeffrey R.
AU - Higgins, Gerald A.
AU - Zheng, Gen
AU - Creekmore, Amy
AU - Wiley, John W.
AU - Verdone, James E.
AU - Veltri, Robert W.
AU - Pienta, Kenneth J.
AU - Coffey, Donald S.
AU - Athey, Brian D.
AU - Dinov, Ivo D.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/13
Y1 - 2018/12/13
N2 - 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.
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 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.
UR - http://www.scopus.com/inward/record.url?scp=85058951388&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058951388&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2018.00304
DO - 10.1109/CVPRW.2018.00304
M3 - Conference contribution
AN - SCOPUS:85058951388
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 2353
EP - 2361
BT - Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
PB - IEEE Computer Society
Y2 - 18 June 2018 through 22 June 2018
ER -