论文标题
嵌入式 - 通过学习偏移和聚类带宽的同时细胞分割和跟踪
EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths
论文作者
论文摘要
对细胞行为的系统分析需要自动方法进行细胞分割和跟踪。尽管深度学习已成功地用于细胞分割的任务，但使用深度学习的同时细胞分割和跟踪的方法很少。在这里，我们提出了嵌入式track，这是一种用于同时细胞分割和跟踪的单个卷积神经网络，可预测易于解释嵌入。作为嵌入，细胞像素的偏移到其细胞中心，并学习了带宽。我们从单元跟踪挑战中基准了我们的九个2D数据集的方法，在该挑战中，我们的方法在前三名参赛者中的九个数据集中的七个中执行，包括三个前1名表演。源代码可在https://git.scc.kit.edu/kit-loe-ge/embedtrack上公开获得。
A systematic analysis of the cell behavior requires automated approaches for cell segmentation and tracking. While deep learning has been successfully applied for the task of cell segmentation, there are few approaches for simultaneous cell segmentation and tracking using deep learning. Here, we present EmbedTrack, a single convolutional neural network for simultaneous cell segmentation and tracking which predicts easy to interpret embeddings. As embeddings, offsets of cell pixels to their cell center and bandwidths are learned. We benchmark our approach on nine 2D data sets from the Cell Tracking Challenge, where our approach performs on seven out of nine data sets within the top 3 contestants including three top 1 performances. The source code is publicly available at https://git.scc.kit.edu/kit-loe-ge/embedtrack.
