当前位置:科学网首页 > 小柯机器人 >详情
细胞追踪挑战:10年的客观基准测试
作者:小柯机器人 发布时间:2023/5/28 22:06:42

西班牙纳瓦拉大学Carlos Ortiz-de-Solórzano和捷克共和国马萨里克大学Michal Kozubek合作,综述了细胞追踪挑战,10年的客观基准测试。相关文章2023年5月18日在线发表于《自然—方法学》杂志上。

据介绍,细胞追踪挑战是一项正在进行的基准测试计划,已成为细胞分割和追踪算法开发的参考。

研究人员介绍了自2017年报告以来在挑战中引入的大量改进。其中包括创建一个新的仅限分割基准,使用增加其多样性和复杂性的新数据集丰富数据集存储库,以及基于最具竞争力的结果创建一个标准参考语料库,这将对基于数据的深度学习策略很有帮助。

此外,研究人员还介绍了最新的细胞分割和跟踪排行榜,深入分析了最先进方法的性能与数据集和注释的特性之间的关系,以及关于最佳方法可推广性和可重用性的两项新颖而深入的研究。

总之,这些研究为传统和基于机器学习的细胞分割和跟踪算法的开发人员和用户提供了重要的实用结论。

附:英文原文

Title: The Cell Tracking Challenge: 10years of objective benchmarking

Author: Maka, Martin, Ulman, Vladimr, Delgado-Rodriguez, Pablo, Gmez-de-Mariscal, Estibaliz, Neasov, Tereza, Guerrero Pea, Fidel A., Ren, Tsang Ing, Meyerowitz, Elliot M., Scherr, Tim, Lffler, Katharina, Mikut, Ralf, Guo, Tianqi, Wang, Yin, Allebach, Jan P., Bao, Rina, Al-Shakarji, Noor M., Rahmon, Gani, Toubal, Imad Eddine, Palaniappan, Kannappan, Lux, Filip, Matula, Petr, Sugawara, Ko, Magnusson, Klas E. G., Aho, Layton, Cohen, Andrew R., Arbelle, Assaf, Ben-Haim, Tal, Raviv, Tammy Riklin, Isensee, Fabian, Jger, Paul F., Maier-Hein, Klaus H., Zhu, Yanming, Ederra, Cristina, Urbiola, Ainhoa, Meijering, Erik, Cunha, Alexandre, Muoz-Barrutia, Arrate, Kozubek, Michal, Ortiz-de-Solrzano, Carlos

Issue&Volume: 2023-05-18

Abstract: The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

DOI: 10.1038/s41592-023-01879-y

Source: https://www.nature.com/articles/s41592-023-01879-y

期刊信息

Nature Methods:《自然—方法学》,创刊于2004年。隶属于施普林格·自然出版集团,最新IF:47.99
官方网址:https://www.nature.com/nmeth/
投稿链接:https://mts-nmeth.nature.com/cgi-bin/main.plex