A benchmark for comparison of cell tracking algorithms

By Martin Maška, V. Ulman, David Svoboda, P. Matula, P. Matula, Cristina Ederra, Ainhoa Urbiola, Tomás España, Subramanian Venkatesan, D. M. Balak and 20 more. 2014

439 citations recorded by the source corpus.

Motivation: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. Results: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately. Availability and implementation: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge. Contact: codesolorzano@unav.es Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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. Franz Hlawatsch
4. Paolo Braca
5. Moe Z. Win
6. Florian Meyer
7. Jason L. Williams
8. Roslyn A. Lau

In Biophysics

1. Lin F. Yang
2. Fuyong Xing
3. Won-Dong Jang
4. Chang-Su Kim

In Computer Vision and Pattern Recognition

1. Lin F. Yang
2. Fuyong Xing
3. Moe Z. Win
4. Jason L. Williams
5. Won-Dong Jang
6. Chang-Su Kim

In Evaluation & Benchmarks

1. Lin F. Yang
2. Won-Dong Jang
3. Chang-Su Kim

Citations in the corpus

• Message Passing Algorithms for Scalable Multitarget Tracking (2018, positive): “Since the first MTT methods were introduced for aerospace surveillance some 40 years ago [5], [6], the field has experienced an intense development, and MTT methods have proven useful in further surveillance scenarios [7]–[9] as well as in biomedical analytics [10]–[12].”
• Interactive Image Segmentation via Backpropagating Refinement Scheme (2019, methodology): “We assess the performance of the interactive U-Net on the two test sequences in the PhC-U373 dataset [33].”
• Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “The ISBI cell tracking challenge [318] benchmark contains 48 time-lapse sequences (half for training and the other for testing), which can be used to evaluate cell segmentation or tracking algorithms.”