论文标题
延时显微镜中的3D核的半监督分割和基于图的跟踪
Semi supervised segmentation and graph-based tracking of 3D nuclei in time-lapse microscopy
论文作者
论文摘要
我们提出了一种新型的弱监督方法，以利用过度分段的图像来改善3D分段核的边界。这是由于观察到当前最新的深度学习方法不会导致训练数据弱注释时准确的边界。为此，对3D U-NET进行了训练，以获取细胞核的质心，并与简单的线性迭代聚类（SLIC）Supervoxel算法集成，该算法可更好地遵守群集边界。为了跟踪这些分段的核，我们的算法利用了描述核分裂和凋亡过程的相对核位置。与2019年细胞跟踪挑战（CTC）的最新方法相比，提出的算法管道可在IEEE ISBI CTC2020中的最新性能中获得更好的分割性能，同时使用了很少的Pixel-Wise wise wise的数据。提供了详细的实验结果，并且源代码可在GitHub上获得。
We propose a novel weakly supervised method to improve the boundary of the 3D segmented nuclei utilizing an over-segmented image. This is motivated by the observation that current state-of-the-art deep learning methods do not result in accurate boundaries when the training data is weakly annotated. Towards this, a 3D U-Net is trained to get the centroid of the nuclei and integrated with a simple linear iterative clustering (SLIC) supervoxel algorithm that provides better adherence to cluster boundaries. To track these segmented nuclei, our algorithm utilizes the relative nuclei location depicting the processes of nuclei division and apoptosis. The proposed algorithmic pipeline achieves better segmentation performance compared to the state-of-the-art method in Cell Tracking Challenge (CTC) 2019 and comparable performance to state-of-the-art methods in IEEE ISBI CTC2020 while utilizing very few pixel-wise annotated data. Detailed experimental results are provided, and the source code is available on GitHub.
