Abstract
Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D deep learning nuclei segmentation method using synthetic 3D volumes for training. A set of synthetic volumes and the corresponding groundtruth are generated using spatially constrained cycle-consistent adversarial networks. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully for various data sets.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 |
| Publisher | IEEE Computer Society |
| Pages | 2302-2310 |
| Number of pages | 9 |
| Volume | 2018-June |
| ISBN (Electronic) | 9781538661000 |
| DOIs | |
| State | Published - Dec 13 2018 |
| Event | 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 - Salt Lake City, United States Duration: Jun 18 2018 → Jun 22 2018 |
Other
| Other | 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 |
|---|---|
| Country | United States |
| City | Salt Lake City |
| Period | 6/18/18 → 6/22/18 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
Cite this
Three dimensional fluorescence microscopy image synthesis and segmentation. / Fu, Chichen; Lee, Soonam; Ho, David Joon; Han, Shuo; Salama, Paul; Dunn, Kenneth; Delp, Edward J.
Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018. Vol. 2018-June IEEE Computer Society, 2018. p. 2302-2310 8575470.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Three dimensional fluorescence microscopy image synthesis and segmentation
AU - Fu, Chichen
AU - Lee, Soonam
AU - Ho, David Joon
AU - Han, Shuo
AU - Salama, Paul
AU - Dunn, Kenneth
AU - Delp, Edward J.
PY - 2018/12/13
Y1 - 2018/12/13
N2 - Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D deep learning nuclei segmentation method using synthetic 3D volumes for training. A set of synthetic volumes and the corresponding groundtruth are generated using spatially constrained cycle-consistent adversarial networks. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully for various data sets.
AB - Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D deep learning nuclei segmentation method using synthetic 3D volumes for training. A set of synthetic volumes and the corresponding groundtruth are generated using spatially constrained cycle-consistent adversarial networks. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully for various data sets.
UR - http://www.scopus.com/inward/record.url?scp=85060860204&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060860204&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2018.00298
DO - 10.1109/CVPRW.2018.00298
M3 - Conference contribution
AN - SCOPUS:85060860204
VL - 2018-June
SP - 2302
EP - 2310
BT - Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
PB - IEEE Computer Society
ER -