Cell segmentation, tracking, and mitosis detection using temporal context

Authors:
Fuxing Yang;Michael A. Mackey;Fiorenza Ianzini;Greg Gallardo;Milan Sonka
Affiliations:
Department of Electrical and Computer Engineering;Departments of Pathology and Biomedical Engineering;Departments of Pathology and Biomedical Engineering;Department of Electrical and Computer Engineering;Department of Electrical and Computer Engineering
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

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Abstract

The Large Scale Digital Cell Analysis System (LSDCAS) developed at the University of Iowa provides capabilities for extended-time live cell image acquisition. This paper presents a new approach to quantitative analysis of live cell image data. By using time as an extra dimension, level set methods are employed to determine cell trajectories from 2D + time data sets. When identifying the cell trajectories, cell cluster separation and mitotic cell detection steps are performed. Each of the trajectories corresponds to the motion pattern of an individual cell in the data set. At each time frame, number of cells, cell locations, cell borders, cell areas, and cell states are determined and recorded. The proposed method can help solving cell analysis problems of general importance including cell pedigree analysis and cell tracking. The developed method was tested on cancer cell image sequences and its performance compared with manually-defined ground truth. The similarity Kappa Index is 0.84 for segmentation area and the signed border positioning segmentation error is 1.6 ± 2.1 μm.