Cell segmentation for multi-modal microscopy images remains a challenge due
to the complex textures, patterns, and cell shapes in these images. To tackle
the problem, we first develop an automatic cell classification pipeline to
label the microscopy images based on their low-level image characteristics, and
then train a classification model based on the category labels. Afterward, we
train a separate segmentation model for each category using the images in the
corresponding category. Besides, we further deploy two types of segmentation
models to segment cells with roundish and irregular shapes respectively.
Moreover, an efficient and powerful backbone model is utilized to enhance the
efficiency of our segmentation model. Evaluated on the Tuning Set of NeurIPS
2022 Cell Segmentation Challenge, our method achieves an F1-score of 0.8795 and
the running time for all cases is within the time tolerance.

多模态显微图像的细胞分割仍然是一个挑战，为了解决这个问题，我们首先开发了一个基于细胞的低级图像特征的自动分类流程，然后训练了一个基于类别标签的分类模型。然后，我们针对每个类别训练了一个单独的分割模型，使用对应类别中的图像。此外，我们还部署了两种类型的分割模型，分别用于分割具有圆形和不规则形状的细胞。此外，为了增强分割模型的效率，我们采用了一种高效且强大的主干模型。在 NeurIPS 2022 Cell Segmentation Challenge 的调优集上评估，我们的方法达到了 F1 得分 0.8795，所有情况的运行时间都在时间容限范围内。