Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Hidden Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cell segmentation, tracking, and mitosis detection using temporal context
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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We propose a fully-automated mitosis event detector using hidden conditional random fields for cell populations imaged with time-lapse phase contrast microscopy. The method consists of two stages that jointly optimize recall and precision. First, we apply model-based microscopy image preconditioning and volumetric segmentation to identifY candidate spatiotemporal sub-regions in the input image sequence where mitosis potentially occurred. Then, we apply a learned hidden conditional random field classifier to classifY each candidate sequence as mitosis or not. The proposed detection method achieved 95% precision and 85% recall in very challenging image sequences of multipolar-shaped C3H10T1/2 mesenchymal stem cells. The superiority of the method was further demonstrated by comparisons with conditional random field and support vector machine classifiers. Moreover, the proposed method does not depend on empirical parameters, ad hoc image processing, or cell tracking; and can be straightforwardly adapted to different cell types.