Multiple Nuclei Tracking Using Integer Programming for Quantitative Cancer Cell Cycle Analysis

By Fuhai Li, Xiaobo Zhou, Jinwen Ma, Stephen T. C. Wong. 2010

138 citations recorded by the source corpus.

Automated cell segmentation and tracking are critical for quantitative analysis of cell cycle behavior using time-lapse fluorescence microscopy. However, the complex, dynamic cell cycle behavior poses new challenges to the existing image segmentation and tracking methods. This paper presents a fully automated tracking method for quantitative cell cycle analysis. In the proposed tracking method, we introduce a neighboring graph to characterize the spatial distribution of neighboring nuclei, and a novel dissimilarity measure is designed based on the spatial distribution, nuclei morphological appearance, migration, and intensity information. Then, we employ the integer programming and division matching strategy, together with the novel dissimilarity measure, to track cell nuclei. We applied this new tracking method for the tracking of HeLa cancer cells over several cell cycles, and the validation results showed that the high accuracy for segmentation and tracking at 99.5% and 90.0%, respectively. The tracking method has been implemented in the cell-cycle analysis software package, DCELLIQ, which is freely available.

Subfields: Artificial Intelligence, Biophysics, Media Technology, Medical Imaging, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lin F. Yang
2. Fuyong Xing
3. Carlos Ortiz-de-Solorzano
4. Arrate Muñoz-Barrutia
5. Erik H. W. Meijering
6. Oleh Dzyubachyk
7. Joakim Jaldén
8. Klas E. G. Magnusson

In Biophysics

1. Fuyong Xing
2. Lin F. Yang
3. Erik H. W. Meijering
4. Carlos Ortiz-de-Solorzano
5. Arrate Muñoz-Barrutia
6. Oleh Dzyubachyk
7. Martin Maška
8. Andrey Kan

In Media Technology

1. Lin F. Yang
2. Fuyong Xing
3. Erik H. W. Meijering
4. Oleh Dzyubachyk
5. Oren Z. Kraus
6. Brendan J. Frey

In Medical Imaging

1. Lin F. Yang
2. Fuyong Xing
3. Joakim Jaldén
4. Klas E. G. Magnusson
5. Oren Z. Kraus
6. Brendan J. Frey

In Computational Biology & Bioinformatics

1. Lin F. Yang
2. Erik H. W. Meijering
3. Fuyong Xing

Citations in the corpus

• A benchmark for comparison of cell tracking algorithms (2014, methodology): “Subsequently, an optimization strategy, such as multiple-hypothesis tracking (Chenouard et al. , 2013), integer programming (Li et al. , 2010), dynamic programming (Magnusson and Jalde ´ n, 2012) or coupled minimum-cost flow tracking (Padfield et al. , 2011), is used to determine the most likely…”
• Segmentation and Shape Tracking of Whole Fluorescent Cells Based on the Chan–Vese Model (2013, background): “Nevertheless, with increasing cell density, the temporal association step often requires sophisticated strategies to deal with one-to-many and many-to-one matching problems effectively [12], [13], [22], [23].”
• Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “ple, based on DT-generated markers, watershed is applied to distance maps [28]–[30], [50], intensity-fused distance maps [31], [32], and gradient-weighted distance maps [33] for nu-”