Scholarlink

<p>Accurate tracking of live cells using video microscopy recordings remains a challenging task for popular state-of-the-art image processing-based object tracking methods. In recent years, many applications have attempted to integrate deep-learning frameworks for this task, but most still heavily rely on consecutive frame-based tracking or other premises that hinder generalized learning. To address this issue, we aimed to develop a novel deep-learning-based tracking method that assumes cells can be tracked by their spatio-temporal neighborhood, without a restriction to consecutive frames. The proposed method has the additional benefit that the motion patterns of the cells can be learned by the predictor without any prior assumptions, and it has the potential to handle a large number of video frames with heavy artifacts. The efficacy of the proposed method is demonstrated through biologically motivated validation strategies and compared against multiple state-of-the-art cell tracking methods on budding yeast recordings and simulated samples.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing yeast cell tracking with a time-symmetric deep learning approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月13日

查看全文
  • Gergely Szabó,
  • Paolo Bonaiuti,
  • Andrea Ciliberto,
  • András Horváth

摘要

Accurate tracking of live cells using video microscopy recordings remains a challenging task for popular state-of-the-art image processing-based object tracking methods. In recent years, many applications have attempted to integrate deep-learning frameworks for this task, but most still heavily rely on consecutive frame-based tracking or other premises that hinder generalized learning. To address this issue, we aimed to develop a novel deep-learning-based tracking method that assumes cells can be tracked by their spatio-temporal neighborhood, without a restriction to consecutive frames. The proposed method has the additional benefit that the motion patterns of the cells can be learned by the predictor without any prior assumptions, and it has the potential to handle a large number of video frames with heavy artifacts. The efficacy of the proposed method is demonstrated through biologically motivated validation strategies and compared against multiple state-of-the-art cell tracking methods on budding yeast recordings and simulated samples.