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3D Centroidnet: Nuclei Centroid Detection with Vector Flow Voting

View ORCID ProfileLiming Wu, View ORCID ProfileAlain Chen, View ORCID ProfilePaul Salama, View ORCID ProfileKenneth W. Dunn, View ORCID ProfileEdward J. Delp
doi: https://doi.org/10.1101/2022.07.21.500996
Liming Wu
*Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette, Indiana, USA
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  • For correspondence: wu1114@purdue.edu
Alain Chen
*Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette, Indiana, USA
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Paul Salama
†Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis, Indiana, USA
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Kenneth W. Dunn
‡Division of Nephrology School of Medicine Indiana University Indianapolis, Indiana, USA
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Edward J. Delp
*Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette, Indiana, USA
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ABSTRACT

Automated microscope systems are increasingly used to collect large-scale 3D image volumes of biological tissues. Since cell boundaries are seldom delineated in these images, detection of nuclei is a critical step for identifying and analyzing individual cells. Due to the large intra-class variability in nuclei morphology and the difficulty of generating ground truth annotations, accurate nuclei detection remains a challenging task. We propose a 3D nuclei centroid detection method by estimating the “vector flow” volume where each voxel represents a 3D vector pointing to its nearest nuclei centroid in the corresponding microscopy volume. We then use a voting mechanism to estimate the 3D nuclei centroids from the “vector flow” volume. Our system is trained on synthetic microscopy volumes and tested on real microscopy volumes. The evaluation results indicate our method outperforms other methods both visually and quantitatively.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • This work was partially supported by a George M. O’Brien Award from the National Institutes of Health under grant NIH/NIDDK P30 DK079312 and the endowment of the Charles William Harrison Distinguished Professorship at Purdue University.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted July 22, 2022.
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3D Centroidnet: Nuclei Centroid Detection with Vector Flow Voting
Liming Wu, Alain Chen, Paul Salama, Kenneth W. Dunn, Edward J. Delp
bioRxiv 2022.07.21.500996; doi: https://doi.org/10.1101/2022.07.21.500996
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3D Centroidnet: Nuclei Centroid Detection with Vector Flow Voting
Liming Wu, Alain Chen, Paul Salama, Kenneth W. Dunn, Edward J. Delp
bioRxiv 2022.07.21.500996; doi: https://doi.org/10.1101/2022.07.21.500996

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