Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy

By Xiaowei Chen, Xiaobo Zhou, Stephen T. C. Wong. IEEE Transactions on Biomedical Engineering, 2006

373 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/41ae827281ac2949ed19a60890b047d02f1212da

How researchers see this paper

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

In Biophysics

1. Fuyong Xing
2. Lin F. Yang
3. Jierong Cheng
4. Jagath Rajapakse
5. Houqiang Li
6. Xiaodong Yang
7. Xiaobo Zhou
8. Erik H. W. Meijering

In Computer Vision and Pattern Recognition

1. Lin F. Yang
2. Fuyong Xing
3. Houqiang Li
4. Xiaodong Yang
5. Xiaobo Zhou
6. Tim Becker
7. Jinwen Ma
8. Jierong Cheng

In Media Technology

1. Lin F. Yang
2. Fuyong Xing
3. Jierong Cheng
4. Jagath Rajapakse
5. Houqiang Li
6. Xiaodong Yang
7. Xiaobo Zhou
8. Oleh Dzyubachyk

Citations in the corpus

• Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Microscopy (2006, methodology): “Index Terms—Cell segmentation, cell tracking, Kalman filter, mean shift, watershed.”
• Segmentation of Clustered Nuclei With Shape Markers and Marking Function (2009, background): “Region merging approaches could be based on shape or sizes of cells or nuclei [8], [16], [17].”
• Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, background): “of nuclei segmentation [171]–[173], and further analysis is conducted to separate touching or overlapping nuclei.”
• Multiple Nuclei Tracking Using Integer Programming for Quantitative Cancer Cell Cycle Analysis (2010, background): “Broadly speaking, current cell tracking approaches can be classified into two categories: 1) the detection and segmentation based tracking and 2) the evolving model based tracking [6], [7].”
• An Objective Comparison of Cell Tracking Algorithms (2017, neutral): “The methods range from simple thresholding 9,10 , hysteresis thresholding 11 , edge detection 12 , or shape matching 13,14 , to more sophisticated region growing 15–17 , machine learning 18,19 , or energy minimization 20–26 approaches.”
• A Novel Validation Algorithm Allows for Automated Cell Tracking and the Extraction of Biologically Meaningful Parameters (2011, methodology): “Among the methods previously used to construct reference data were manual lineage construction from real time-lapse data [8,12,14,28], often performed on smaller subsets [10,19,20], artificially created data [16,22] as well as manual mitosis detection [39].”