论文标题
通过结合标记控制的流域和深度学习来分割细胞分割
Cell Segmentation by Combining Marker-Controlled Watershed and Deep Learning
论文作者
论文摘要
我们提出了一种细胞分割方法，用于分析密集聚类细胞的图像。该方法结合了标记控制的流域转化和卷积神经网络（CNN）的优势。我们在三个细胞跟踪挑战（CTC）数据集中证明了通过不同采集技术捕获的聚类单元格的方法普遍性和高性能。对于所有测试的数据集，我们的方法在细胞检测和分割中都达到了最高的性能。根据一系列实验，我们观察到：（1）预测分水岭函数和分割函数可显着提高分割的准确性。 （2）可以独立学习这两个功能。 （3）通过缩放和刚性几何变换训练数据的增强优于涉及弹性转换的增强。我们的方法易于使用，并且可以很好地概括具有最先进性能的各种数据。
We propose a cell segmentation method for analyzing images of densely clustered cells. The method combines the strengths of marker-controlled watershed transformation and a convolutional neural network (CNN). We demonstrate the method universality and high performance on three Cell Tracking Challenge (CTC) datasets of clustered cells captured by different acquisition techniques. For all tested datasets, our method reached the top performance in both cell detection and segmentation. Based on a series of experiments, we observed: (1) Predicting both watershed marker function and segmentation function significantly improves the accuracy of the segmentation. (2) Both functions can be learned independently. (3) Training data augmentation by scaling and rigid geometric transformations is superior to augmentation that involves elastic transformations. Our method is simple to use, and it generalizes well for various data with state-of-the-art performance.
