Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy.
The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the development of tissues, organs or entire organisms. However, the segmentation of touching cells in images with a low signal-to-noise-ratio is still a chal...
Main Authors: | Tim Scherr, Katharina Löffler, Moritz Böhland, Ralf Mikut |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2020-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0243219 |
Similar Items
-
GRAPH CNN WITH RADIUS DISTANCE FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDINGS TLS POINT CLOUDS
by: C. Morbidoni, et al.
Published: (2020-09-01) -
CNN-Based Segmentation and Tracking in Magnetic Resonance and Ultrasound Image Sequence
by: Cheng-WeiYang, et al.
Published: (2019) -
Graph based approaches for image segmentation and object tracking
by: Wang, Xiaofang
Published: (2015) -
Feature-graph Based Image Segmentation and DisparityPropagation in Stereo Matching
by: Hong-Shang Lin, et al.
Published: (2011) -
The matching polynomial of a distance-regular graph
by: Robert A. Beezer, et al.
Published: (2000-01-01)