CHOTA: A Higher Order Accuracy Metric for Cell Tracking
Institute for Information Processing – Leibniz University Hannover（汉诺威莱布尼茨大学信息处理研究所）；IT4Innovations, VSB – Technical University of Ostrava（俄斯特拉发技术大学VSB IT4Innovations研究院）
细胞追踪结果的评估引导着追踪方法的发展，对生物医学研究产生重大影响。这一过程通过评估度量以定量方式实现。遗憾的是，当前度量偏向局部正确性，对全局一致性的奖励较弱，阻碍了高层次的生物学分析。为了同时促进全局一致性，我们提出了CHOTA度量（Cell-specific Higher Order Tracking Accuracy，细胞特异的高阶追踪精度），它统一评估细胞追踪的所有相关方面：细胞检测与局部关联、全局一致性以及谱系追踪。我们通过引入“轨迹”一词的新定义来实现这一点，该定义包含完整的细胞谱系，并将其纳入通用多目标追踪中已广泛建立的HOTA度量。此外，我们提供了对当代细胞追踪度量的详细综述，以比较我们提出的新CHOTA度量并展示其优势。所有度量均在最新的真实数据细胞追踪结果以及模拟特定追踪错误的合成结果上进行了广泛评估。我们表明，CHOTA对所有追踪错误都敏感，并能很好地指示一种方法重建细胞完整谱系的生物学相关能力。它为当前细胞追踪中使用的度量引入了一种稳健且全面的替代方案。Python代码可在https://github.com/CellTrackingChallenge/py-ctcmetrics获取。
英文摘要：
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 .
Trackastra: Transformer-based cell tracking for live-cell microscopy
Institute of Bioengineering, School of Life Sciences, EPFL（EPFL生命科学学院生物工程研究所）；Center for Scalable Data Analytics and AI (ScaDS.AI)（可扩展数据分析与人工智能中心（ScaDS.AI））；Faculty of Computer Science, TU Dresden（德累斯顿工业大学计算机科学学院）
SPF-CellTracker: Tracking multiple cells with strongly-correlated moves using a spatial particle filter
Kanazawa University（金泽大学）；Kyushu Institute of Technology（九州工业大学）；University of Tokyo（东京大学）；Kyushu University（九州大学）；Institute of Statistical Mathematics（统计数理研究所）；JST-CREST（日本科学技术振兴机构CREST）
PyUAT: Open-source Python framework for efficient and scalable cell tracking
Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich（于利希研究中心生物与地球科学研究所IBG-1生物技术分部）；Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University（亚琛工业大学计算系统生物技术研究所（AVT.CSB））
Cell as Point: One-Stage Framework for Efficient Cell Tracking
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Department of Computer Science, University of Maryland College Park（马里兰大学学院公园分校计算机科学系）；Microsoft（微软公司）
TransientTrack: Advanced Multi-Object Tracking and Classification of Cancer Cells with Transient Fluorescent Signals
Institute for Biomedical Informatics, Faculty of Medicine；University Hospital Cologne, University of Cologne, Cologne, Germany.；Faculty of Mathematics；Natural Sciences, University of Cologne, Cologne, Germany.；Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine；Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany；Charité Comprehensive Cancer Center, Charité Universitätsmedizin, Berlin, Germany.；Centre for Genomic Regulation, Barcelona, Spain.
Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages
Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association（亥姆霍兹联合会马克斯·德尔布吕克分子医学中心）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Sloan Kettering Cancer Center（斯隆·凯特琳癌症中心）；HHMI Janelia Research Campus（霍华德·休斯医学研究所珍妮莉亚研究园区）
DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine
Laboratoire Jean-Perrin UMR 8237, Sorbonne Université（索邦大学让-佩兰实验室）；SABILAb；CMAP, Ecole Polytechnique, Université Paris-Saclay（巴黎萨克雷大学综合理工学院应用数学中心）；Heuritech
母机是一种流行的微流体设备，可以通过显微镜对数千个细胞进行长期的延时成像。它已成为单细胞水平定量分析和表征许多细胞过程，如基因表达和调控，诱变或对抗生素的反应的有价值的工具。对这些实验产生的大量数据进行自动化和定量分析现在是限制性的一步。特别是相衬显微镜成像的细菌细胞的分割和跟踪--与高通量数据兼容的错误率--是一个具有挑战性的问题。在这项工作中，我们描述了一种新的配方的多目标跟踪问题，其中跟踪是通过回归的细菌的位移，允许同时跟踪多个细菌，尽管他们的生长和分裂随着时间的推移。我们的方法执行联合分割和跟踪，利用顺序信息，以提高分割精度。我们引入了一个深度神经网络架构，利用自注意机制，产生极低的跟踪错误率和分割错误率。与最先进的方法相比，我们展示了卓越的性能和速度。我们的方法被命名为DistNet，代表DISTance+Displacement Segmentation and Tracking Network。虽然这种方法是特别适合于母机显微镜数据，其一般的联合跟踪和分割配方可以应用于许多其他问题与不同的几何形状。
Object Tracking in a $360^o$ View: A Novel Perspective on Bridging the Gap to Biomedical Advancements
Stanford Center for Artificial Intelligence in Medicine and Imaging, Stanford University（斯坦福大学医学与影像人工智能中心）；Harvard University（哈佛大学）；School of Computing, The University of Georgia（佐治亚大学计算学院）；Department of Cellular Biology, The University of Georgia（佐治亚大学细胞生物学系）
CellTrack R-CNN: A Novel End-To-End Deep Neural Network for Cell Segmentation and Tracking in Microscopy Images
University of Sydney（悉尼大学）；University of New South Wales（新南威尔士大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Sydney Nano Institute（悉尼纳米研究所）
Cell Behavior Video Classification Challenge, a benchmark for computer vision methods in time-lapse microscopy
Euler Institute, Faculty of Informatics, Università della Svizzera italiana（欧拉研究所，信息学院，瑞士大学）；International Center for Advanced Computing in Medicine (ICAM), University of Pavia（国际医学先进计算中心（ICAM），帕维亚大学）；Imaging Platform, ACRF INCITe Centre, Garvan Institute of Medical Research（成像平台，ACRF INCITe中心，嘉文医学研究所）；Tacoma School of Engineering & Technology, University of Washington（塔科马工程与技术学院，华盛顿大学）；Data Science, Institute for Computing and Information Sciences, Radboud University（数据科学，计算与信息科学研究所，拉德堡德大学）；Imaging Core, Lerner Research Institute, Cleveland Clinic（成像核心，勒纳研究研究所，克利夫兰诊所）；Institute of Biomedical Engineering, University of Toronto（生物医学工程研究所，多伦多大学）；Center for Research in Computer Vision, University of Central Florida（计算机视觉研究中心，佛罗里达大学）；Computational Biology Group, Data Science Platform, Garvan Institute of Medical Research（计算生物学小组，数据科学平台，嘉文医学研究所）
Accurate Tracking of Arabidopsis Root Cortex Cell Nuclei in 3D Time-Lapse Microscopy Images Based on Genetic Algorithm
Graduate School of Science and Technology, Nara Institute of Science and Technology（奈良科学技术大学研究生院）；Bioresource Engineering Laboratory, Ishikawa Prefectural University（石川县立大学生物资源工程实验室）；Department of Engineering Informatics, Osaka Electro-Communication University（大阪电讯大学工程信息学系）；Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences（国家自然科学研究院生命与生活系统探索研究中心）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths
Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院自动化与应用信息研究所）；Institute of Biological and Chemical Systems - Biological Information Processing, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院生物与化学系统研究所——生物信息处理分部）
