最多等待 10 秒，若超时请稍后重试。
KAIST.
Boston Children's Hospital, Harvard Medical School.
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2023 Jun;2023:227-236. doi: 10.1109/cvpr52729.2023.00030. Epub 2023 Aug 22.
Analyzing the dynamic changes of cellular morphology is important for understanding the various functions and characteristics of live cells, including stem cells and metastatic cancer cells. To this end, we need to track all points on the highly deformable cellular contour in every frame of live cell video. Local shapes and textures on the contour are not evident, and their motions are complex, often with expansion and contraction of local contour features. The prior arts for optical flow or deep point set tracking are unsuited due to the fluidity of cells, and previous deep contour tracking does not consider point correspondence. We propose the first deep learning-based tracking of cellular (or more generally viscoelastic materials) contours with point correspondence by fusing dense representation between two contours with cross attention. Since it is impractical to manually label dense tracking points on the contour, unsupervised learning comprised of the mechanical and cyclical consistency losses is proposed to train our contour tracker. The mechanical loss forcing the points to move perpendicular to the contour effectively helps out. For quantitative evaluation, we labeled sparse tracking points along the contour of live cells from two live cell datasets taken with phase contrast and confocal fluorescence microscopes. Our contour tracker quantitatively outperforms compared methods and produces qualitatively more favorable results. Our code and data are publicly available at https://github.com/JunbongJang/contour-tracking/.
分析细胞形态的动态变化对于理解活细胞（包括干细胞和转移性癌细胞）的各种功能和特性至关重要。为此，我们需要在活细胞视频的每一帧中跟踪高度可变形细胞轮廓上的所有点。轮廓上的局部形状和纹理并不明显，并且它们的运动很复杂，局部轮廓特征常常会有扩张和收缩。由于细胞的流动性，现有的光流或深度点集跟踪技术并不适用，并且以前的深度轮廓跟踪没有考虑点对应关系。我们提出了第一种基于深度学习的细胞（或更一般的粘弹性材料）轮廓跟踪方法，通过交叉注意力融合两个轮廓之间的密集表示来实现点对应。由于在轮廓上手动标记密集跟踪点不切实际，因此我们提出了由机械一致性损失和循环一致性损失组成的无监督学习方法来训练我们的轮廓跟踪器。迫使点垂直于轮廓移动的机械损失有效地起到了帮助作用。为了进行定量评估，我们从用相差显微镜和共聚焦荧光显微镜拍摄的两个活细胞数据集中，沿着活细胞轮廓标记了稀疏跟踪点。我们的轮廓跟踪器在定量方面优于比较方法，并产生了质量上更优的结果。我们的代码和数据可在https://github.com/JunbongJang/contour-tracking/上公开获取。
