Consistent Cell Tracking in Multi-frames with Spatio-Temporal Context by Object-Level Warping Loss
Consistent Cell Tracking in Multi-frames with Spatio-Temporal Context by Object-Level Warping Loss
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DOI：
10.1109/wacv51458.2022.00182
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Junya Hayashida;Kazuya Nishimura;Ryoma Bise
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文献类型：
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作者：
Junya Hayashida;Kazuya Nishimura;Ryoma Bise
多目标跟踪是生物医学图像分析中的一个重要问题。大多数方法遵循检测跟踪方法，该方法涉及使用对象检测器并学习检测到的区域的外观特征模型以进行关联。虽然这些方法可以学习外观相似性特征以识别帧之间的相同对象，但是它们难以识别相同的细胞，因为细胞具有相似的外观并且它们的形状随着它们的迁移而改变。此外，细胞往往部分重叠的几个帧。在这种情况下，即使是专业的生物学家也需要时空背景的知识来识别单个细胞。为了解决这种困难的情况下，我们提出了一种细胞跟踪方法，可以有效地使用在多帧的时空背景下，通过使用长期的运动估计和对象级的翘曲损失。我们进行的实验表明，该方法优于国家的最先进的方法在各种条件下对真实的生物图像。
Multi-object tracking is essential in biomedical image analysis. Most methods follow a tracking-by-detection approach that involves using object detectors and learning the appearance feature models of the detected regions for association. Although these methods can learn the appearance similarity features to identify the same objects among frames, they have difficulties identifying the same cells because cells have a similar appearance and their shapes change as they migrate. In addition, cells often partially overlap for several frames. In this case, even an expert biologist would require knowledge of the spatial-temporal context in order to identify individual cells. To tackle such difficult situations, we propose a cell-tracking method that can effectively use the spatial-temporal context in multiple frames by using long-term motion estimation and an object-level warping loss. We conducted experiments showing that the proposed method outperformed state-of-the-art methods under various conditions on real biological images.