title: Cell detection and segmentation using correlation clustering.
abstract: Cell detection and segmentation in microscopy images is important for quantitative high-throughput experiments. We present a learning-based method that is applicable to different modalities and cell types, in particular to cells that appear almost transparent in the images. We first train a classifier to detect (partial) cell boundaries. The resulting predictions are used to obtain superpixels and a weighted region adjacency graph. Here, edge weights can be either positive (attractive) or negative (repulsive). The graph partitioning problem is then solved using correlation clustering segmentation. One variant we newly propose here uses a length constraint that achieves state-of-art performance and improvements in some datasets. This constraint is approximated using non-planar correlation clustering. We demonstrate very good performance in various bright field and phase contrast microscopy experiments.
authors.forename: Chong
authors.initials: C
authors.lastname: Zhang
authors.forename: Julian
authors.initials: J
authors.lastname: Yarkony
authors.forename: Fred A
authors.initials: FA
authors.lastname: Hamprecht
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D058948:Cell Tracking / Q000379:methods*; D006367:HeLa Cells; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008858:Microscopy, Phase-Contrast / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D013382:Subtraction Technique; D015003:Yeasts / Q000166:cytology*