  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.
