Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images
Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images
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DOI：
10.3390/jcm8081159
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发表时间：
2019-08
影响因子：
3.9
通讯作者：
Pengyu Yuan;A. Rezvan;Xiaoyang Li;N. Varadarajan;Hien Van Nguyen
中科院分区：
文献类型：
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作者：
Pengyu Yuan;A. Rezvan;Xiaoyang Li;N. Varadarajan;Hien Van Nguyen
延时显微镜对于定量细胞、亚细胞器和生物分子的动态是必不可少的。生物学家使用不同的荧光标记来标记和跟踪细胞内的亚细胞结构和生物分子。然而，并不是所有的标记都与延时成像兼容，并且标记本身会以不期望的方式干扰细胞。我们假设相位图像具有识别和跟踪细胞内细胞核的必要信息。通过利用传统的斑点检测从染色通道图像生成二进制掩码标签，以及利用深度学习掩码RCNN模型训练检测和分割模型，我们设法仅基于相位图像分割细胞核。当IoU阈值被设置为0.5时，检测平均精度为0.82。从专家的相位图像和地面真实掩模生成的掩模的平均IoU为0.735。在训练期间没有任何真实掩码标签，这足以证明我们的假设。该结果使得能够在不需要外源标记的情况下检测细胞核。
Time lapse microscopy is essential for quantifying the dynamics of cells, subcellular organelles and biomolecules. Biologists use different fluorescent tags to label and track the subcellular structures and biomolecules within cells. However, not all of them are compatible with time lapse imaging, and the labeling itself can perturb the cells in undesirable ways. We hypothesized that phase image has the requisite information to identify and track nuclei within cells. By utilizing both traditional blob detection to generate binary mask labels from the stained channel images and the deep learning Mask RCNN model to train a detection and segmentation model, we managed to segment nuclei based only on phase images. The detection average precision is 0.82 when the IoU threshold is to be set 0.5. And the mean IoU for masks generated from phase images and ground truth masks from experts is 0.735. Without any ground truth mask labels during the training time, this is good enough to prove our hypothesis. This result enables the ability to detect nuclei without the need for exogenous labeling.