TY - GEN

T1 - CHOTA

T2 - 18th European Conference on Computer Vision, ECCV 2024

AU - Kaiser, Timo

AU - Ulman, Vladimír

AU - Rosenhahn, Bodo

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025/5/12

Y1 - 2025/5/12

N2 - The evaluation of cell tracking results steers the development of tracking methods, significantly impacting biomedical research. This is quantitatively achieved by means of evaluation metrics. Unfortunately, current metrics favor local correctness and weakly reward global coherence, impeding high-level biological analysis. To also foster global coherence, we propose the CHOTA metric (Cell-specific Higher Order Tracking Accuracy) which unifies the evaluation of all relevant aspects of cell tracking: cell detections and local associations, global coherence, and lineage tracking. We achieve this by introducing a new definition of the term ‘trajectory’ that includes the entire cell lineage and by including this into the well-established HOTA metric from general multiple object tracking. Furthermore, we provide a detailed survey of contemporary cell tracking metrics to compare our novel CHOTA metric and to show its advantages. All metrics are extensively evaluated on state-of-the-art real-data cell tracking results and synthetic results that simulate specific tracking errors. We show that CHOTA is sensitive to all tracking errors and gives a good indication of the biologically relevant capability of a method to reconstruct the full lineage of cells. It introduces a robust and comprehensive alternative to the currently used metrics in cell tracking. Python code is available at https://github.com/CellTrackingChallenge/py-ctcmetrics.

AB - The evaluation of cell tracking results steers the development of tracking methods, significantly impacting biomedical research. This is quantitatively achieved by means of evaluation metrics. Unfortunately, current metrics favor local correctness and weakly reward global coherence, impeding high-level biological analysis. To also foster global coherence, we propose the CHOTA metric (Cell-specific Higher Order Tracking Accuracy) which unifies the evaluation of all relevant aspects of cell tracking: cell detections and local associations, global coherence, and lineage tracking. We achieve this by introducing a new definition of the term ‘trajectory’ that includes the entire cell lineage and by including this into the well-established HOTA metric from general multiple object tracking. Furthermore, we provide a detailed survey of contemporary cell tracking metrics to compare our novel CHOTA metric and to show its advantages. All metrics are extensively evaluated on state-of-the-art real-data cell tracking results and synthetic results that simulate specific tracking errors. We show that CHOTA is sensitive to all tracking errors and gives a good indication of the biologically relevant capability of a method to reconstruct the full lineage of cells. It introduces a robust and comprehensive alternative to the currently used metrics in cell tracking. Python code is available at https://github.com/CellTrackingChallenge/py-ctcmetrics.

KW - Cell Tracking

KW - Evaluation Metrics

KW - Object Tracking

UR - http://www.scopus.com/inward/record.url?scp=105007787181&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-91721-9_8

DO - 10.1007/978-3-031-91721-9_8

M3 - Conference contribution

AN - SCOPUS:105007787181

SN - 9783031917202

T3 - Lecture Notes in Computer Science

SP - 122

EP - 138

BT - Computer Vision – ECCV 2024 Workshops, Proceedings

A2 - Del Bue, Alessio

A2 - Canton, Cristian

A2 - Pont-Tuset, Jordi

A2 - Tommasi, Tatiana

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 29 September 2024 through 4 October 2024

ER -