title: Unsupervised unstained cell detection by SIFT keypoint clustering and self-labeling algorithm.
abstract: We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field microscopy. Therefore, the algorithm was assessed on images acquired using these two modalities. Five cell lines having in total 37 images and 7250 cells were considered for the evaluation: CHO, L929, Sf21, HeLa, and Bovine cells. The obtained F-measures were between 85.1 and 89.5. Compared to the state-of-the-art, the algorithm achieves very close F-measure to the supervised approaches in much less time.
authors.forename: Firas
authors.initials: F
authors.lastname: Muallal
authors.forename: Simon
authors.initials: S
authors.lastname: Schöll
authors.forename: Björn
authors.initials: B
authors.lastname: Sommerfeldt
authors.forename: Andreas
authors.initials: A
authors.lastname: Maier
authors.forename: Stefan
authors.initials: S
authors.lastname: Steidl
authors.forename: Rainer
authors.initials: R
authors.lastname: Buchholz
authors.forename: Joachim
authors.initials: J
authors.lastname: Hornegger
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D058948:Cell Tracking / Q000379:methods*; D004282:Documentation / Q000379:methods; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013194:Staining and Labeling; D013347:Subcellular Fractions / Q000648:ultrastructure*