Weakly Supervised Cell-Instance Segmentation with Two Types of Weak Labels by Single Instance Pasting
Weakly Supervised Cell-Instance Segmentation with Two Types of Weak Labels by Single Instance Pasting
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DOI：
10.1109/wacv56688.2023.00320
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发表时间：
2023-01
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影响因子：
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通讯作者：
Kazuya Nishimura;Ryoma Bise
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文献类型：
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作者：
Kazuya Nishimura;Ryoma Bise
识别每个细胞边界的细胞实例分割是细胞图像分析中的一项重要任务。虽然基于深度学习的方法在一定数量的训练数据上显示出了很好的性能，但大多数方法都需要完整的注释来显示每个细胞的边界。生成用于细胞分割的注释既耗时又费力。为了降低标注成本，我们提出了一种使用两种弱标签（一种用于细胞类型，另一种用于细胞核位置）的弱监督分割方法。与一般图像不同，这两个标签在相衬图像中很容易获得。由于这两个弱标签无法直接获得细胞实例分割所需的细胞间边界，因此我们提出了基于复制粘贴技术的单实例粘贴方法来生成细胞间边界信息。首先，我们通过计数细胞来定位单细胞区域，并将它们存储在池中。然后，将存储的单细胞区域粘贴到原始图像上，生成细胞间边界。最后，我们用生成的标签训练一个边界估计网络，并用该网络进行实例分割。我们在公共数据集上的评估表明，所提出的方法在我们比较的几种弱监督方法中取得了最好的性能。
Cell instance segmentation that recognizes each cell boundary is an important task in cell image analysis. While deep learning-based methods have shown promising performances with a certain amount of training data, most of them require full annotations that show the boundary of each cell. Generating the annotation for cell segmentation is time-consuming and human labor. To reduce the annotation cost, we propose a weakly supervised segmentation method using two types of weak labels (one for cell type and one for nuclei position). Unlike general images, these two labels are easily obtained in phase-contrast images. The intercellular boundary, which is necessary for cell instance segmentation, cannot be directly obtained from these two weak labels, so to generate the boundary information, we propose a single instance pasting based on the copy-and-paste technique. First, we locate single-cell regions by counting cells and store them in a pool. Then, we generate the intercel-lular boundary by pasting the stored single-cell regions to the original image. Finally, we train a boundary estimation network with the generated labels and perform instance segmentation with the network. Our evaluation on a public dataset demonstrated that the proposed method achieves the best performance among the several weakly supervised methods we compared.