title: A semi-Markov model for mitosis segmentation in time-lapse phase contrast microscopy image sequences of stem cell populations.
abstract: We propose a semi-Markov model trained in a max-margin learning framework for mitosis event segmentation in large-scale time-lapse phase contrast microscopy image sequences of stem cell populations. Our method consists of three steps. First, we apply a constrained optimization based microscopy image segmentation method that exploits phase contrast optics to extract candidate subsequences in the input image sequence that contains mitosis events. Then, we apply a max-margin hidden conditional random field (MM-HCRF) classifier learned from human-annotated mitotic and nonmitotic sequences to classify each candidate subsequence as a mitosis or not. Finally, a max-margin semi-Markov model (MM-SMM) trained on manually-segmented mitotic sequences is utilized to reinforce the mitosis classification results, and to further segment each mitosis into four predefined temporal stages. The proposed method outperforms the event-detection CRF model recently reported by Huh as well as several other competing methods in very challenging image sequences of multipolar-shaped C3H10T1/2 mesenchymal stem cells. For mitosis detection, an overall precision of 95.8% and a recall of 88.1% were achieved. For mitosis segmentation, the mean and standard deviation for the localization errors of the start and end points of all mitosis stages were well below 1 and 2 frames, respectively. In particular, an overall temporal location error of 0.73 ± 1.29 frames was achieved for locating daughter cell birth events.
authors.affiliation: School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China. anan0422@gmail.com
authors.forename: An-An
authors.initials: AA
authors.lastname: Liu
authors.forename: Kang
authors.initials: K
authors.lastname: Li
authors.forename: Takeo
authors.initials: T
authors.lastname: Kanade
mesh_terms: D000465:Algorithms; D000818:Animals; D058948:Cell Tracking / Q000379:methods*; D002478:Cells, Cultured; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008390:Markov Chains; D059630:Mesenchymal Stem Cells / Q000166:cytology* / Q000502:physiology*; D051379:Mice; D008809:Mice, Inbred C3H; D008858:Microscopy, Phase-Contrast / Q000379:methods*; D018715:Microscopy, Video / Q000379:methods; D008938:Mitosis / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D059008:Time-Lapse Imaging / Q000379:methods*