Cell Tracking with Deep Learning and the Viterbi Algorithm
Cell Tracking with Deep Learning and the Viterbi Algorithm
复制标题
利用深度学习和维特比算法进行细胞追踪
DOI：
10.1109/marss.2018.8481231
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Vijay R. Kumar
中科院分区：
文献类型：
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作者：
David E. Hernandez;Steven W. Chen;Elizabeth E. Hunter;E. Steager;Vijay R. Kumar
我们提出了一种细胞跟踪管道，它将深度细胞分割与维特比算法跟踪器相结合，以准确检测和跟踪显微镜视频中的细胞。我们的管道可以处理大的照明变化、细胞的大外观变化以及其他细胞和碎片的严重遮挡。我们首先训练全卷积网络（FCN）来检测细胞，然后使用基于维特比算法的跟踪器跨帧跟踪细胞。我们在以大肠杆菌 (E. coli) 为特征的数据集上评估我们的算法，其中实验目标是使用蓝光固定大肠杆菌，从而由于光照变化较大而使数据集特别具有挑战性。我们的结果表明，尽管存在这些挑战，我们的管道仍然能够准确地检测和跟踪细胞。
We present a cell tracking pipeline that combines deep cell segmentation with a Viterbi algorithm tracker to accurately detect and track cells in microscopy videos. Our pipeline handles large illumination shifts, large appearance variability in the cells, and heavy occlusion from other cells and debris. We first train a Fully Convolutional Network (FCN) to detect the cells, then track the cells across frames using a tracker based on the Viterbi algorithm. We evaluate our algorithm on a dataset featuring Escherichia coli (E. coli) where the experimental goal is to immobilize the E. coli using blue light, thus making the dataset especially challenging due to large illumination shifts. Our results demonstrate that despite these challenges, our pipeline is able to accurately detect and track the cells.