Combinatorics

Image segmentation

Sociology

Pedagogy

Pure mathematics

Modalities

Mathematics

Social science

Biology

Human–computer interaction

Algorithm

Data mining

Machine learning

Rank (graph theory)

Usability

Paleontology

Artificial intelligence

Psychology

Tracking (education)

Context (archaeology)

Computer science

Field (mathematics)

Computer vision

Segmentation

Nature Methods

This analysis describes the results of three Cell Tracking Challenge editions for examining the performance of cell segmentation and tracking algorithms and provides practical feedback for users and developers. We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

An objective comparison of cell-tracking algorithms