Nuclei detection and segmentation of fluorescence microscopy images using three dimensional convolutional neural networks

David Joon Ho, Chicken Fu, Paul Salama, Kenneth Dunn, Edward J. Delp

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

4 Citations (Scopus)

Abstract

Recent advance in fluorescence microscopy enables acquisition of 3D image volumes with better quality and deeper penetration into tissue. In this paper, we describe a 3D method which can detect and segment nuclei in fluorescence microscopy images using convolutional neural networks (CNN). For nuclei detection, a 3D adaptive histogram equalization, a 3D distance transform, and a 3D classification CNN are used to find centers of nuclei. For nuclei segmentation, a 3D segmentation CNN is used which is trained from automatically generated synthetic microscopy volumes and their synthetic ground truth volumes. Our method outperforms other 3D segmentation methods and can detect nuclei successfully on multiple data sets.

Original languageEnglish (US)
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages418-422
Number of pages5
Volume2018-April
ISBN (Electronic)9781538636367
DOIs
StatePublished - May 23 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Other

Other15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
CountryUnited States
CityWashington
Period4/4/184/7/18

Fingerprint

Three-Dimensional Imaging
Fluorescence microscopy
Fluorescence Microscopy
Neural networks
Microscopy
Microscopic examination
Tissue

Keywords

  • Convolutional neural network
  • Fluorescence microscopy
  • Nuclei detection
  • Nuclei segmentation
  • Synthetic volumes

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Ho, D. J., Fu, C., Salama, P., Dunn, K., & Delp, E. J. (2018). Nuclei detection and segmentation of fluorescence microscopy images using three dimensional convolutional neural networks. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (Vol. 2018-April, pp. 418-422). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363606

Nuclei detection and segmentation of fluorescence microscopy images using three dimensional convolutional neural networks. / Ho, David Joon; Fu, Chicken; Salama, Paul; Dunn, Kenneth; Delp, Edward J.

2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Vol. 2018-April IEEE Computer Society, 2018. p. 418-422.

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

Ho, DJ, Fu, C, Salama, P, Dunn, K & Delp, EJ 2018, Nuclei detection and segmentation of fluorescence microscopy images using three dimensional convolutional neural networks. in 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. vol. 2018-April, IEEE Computer Society, pp. 418-422, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, United States, 4/4/18. https://doi.org/10.1109/ISBI.2018.8363606
Ho DJ, Fu C, Salama P, Dunn K, Delp EJ. Nuclei detection and segmentation of fluorescence microscopy images using three dimensional convolutional neural networks. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Vol. 2018-April. IEEE Computer Society. 2018. p. 418-422 https://doi.org/10.1109/ISBI.2018.8363606
Ho, David Joon ; Fu, Chicken ; Salama, Paul ; Dunn, Kenneth ; Delp, Edward J. / Nuclei detection and segmentation of fluorescence microscopy images using three dimensional convolutional neural networks. 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Vol. 2018-April IEEE Computer Society, 2018. pp. 418-422
@inproceedings{a9c937a1d6fc43939ae00264dd10cb2b,
title = "Nuclei detection and segmentation of fluorescence microscopy images using three dimensional convolutional neural networks",
abstract = "Recent advance in fluorescence microscopy enables acquisition of 3D image volumes with better quality and deeper penetration into tissue. In this paper, we describe a 3D method which can detect and segment nuclei in fluorescence microscopy images using convolutional neural networks (CNN). For nuclei detection, a 3D adaptive histogram equalization, a 3D distance transform, and a 3D classification CNN are used to find centers of nuclei. For nuclei segmentation, a 3D segmentation CNN is used which is trained from automatically generated synthetic microscopy volumes and their synthetic ground truth volumes. Our method outperforms other 3D segmentation methods and can detect nuclei successfully on multiple data sets.",
keywords = "Convolutional neural network, Fluorescence microscopy, Nuclei detection, Nuclei segmentation, Synthetic volumes",
author = "Ho, {David Joon} and Chicken Fu and Paul Salama and Kenneth Dunn and Delp, {Edward J.}",
year = "2018",
month = "5",
day = "23",
doi = "10.1109/ISBI.2018.8363606",
language = "English (US)",
volume = "2018-April",
pages = "418--422",
booktitle = "2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018",
publisher = "IEEE Computer Society",
address = "United States",

}

TY - GEN

T1 - Nuclei detection and segmentation of fluorescence microscopy images using three dimensional convolutional neural networks

AU - Ho, David Joon

AU - Fu, Chicken

AU - Salama, Paul

AU - Dunn, Kenneth

AU - Delp, Edward J.

PY - 2018/5/23

Y1 - 2018/5/23

N2 - Recent advance in fluorescence microscopy enables acquisition of 3D image volumes with better quality and deeper penetration into tissue. In this paper, we describe a 3D method which can detect and segment nuclei in fluorescence microscopy images using convolutional neural networks (CNN). For nuclei detection, a 3D adaptive histogram equalization, a 3D distance transform, and a 3D classification CNN are used to find centers of nuclei. For nuclei segmentation, a 3D segmentation CNN is used which is trained from automatically generated synthetic microscopy volumes and their synthetic ground truth volumes. Our method outperforms other 3D segmentation methods and can detect nuclei successfully on multiple data sets.

AB - Recent advance in fluorescence microscopy enables acquisition of 3D image volumes with better quality and deeper penetration into tissue. In this paper, we describe a 3D method which can detect and segment nuclei in fluorescence microscopy images using convolutional neural networks (CNN). For nuclei detection, a 3D adaptive histogram equalization, a 3D distance transform, and a 3D classification CNN are used to find centers of nuclei. For nuclei segmentation, a 3D segmentation CNN is used which is trained from automatically generated synthetic microscopy volumes and their synthetic ground truth volumes. Our method outperforms other 3D segmentation methods and can detect nuclei successfully on multiple data sets.

KW - Convolutional neural network

KW - Fluorescence microscopy

KW - Nuclei detection

KW - Nuclei segmentation

KW - Synthetic volumes

UR - http://www.scopus.com/inward/record.url?scp=85048143200&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048143200&partnerID=8YFLogxK

U2 - 10.1109/ISBI.2018.8363606

DO - 10.1109/ISBI.2018.8363606

M3 - Conference contribution

AN - SCOPUS:85048143200

VL - 2018-April

SP - 418

EP - 422

BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018

PB - IEEE Computer Society

ER -