  The segmentation of cell nuclei in tissue images stained with the blood dye
hematoxylin and eosin (H$\&$E) is essential for various clinical applications
and analyses. Due to the complex characteristics of cellular morphology, a
large receptive field is considered crucial for generating high-quality
segmentation. However, previous methods face challenges in achieving a balance
between the receptive field and computational burden. To address this issue, we
propose LKCell, a high-accuracy and efficient cell segmentation method. Its
core insight lies in unleashing the potential of large convolution kernels to
achieve computationally efficient large receptive fields. Specifically, (1) We
transfer pre-trained large convolution kernel models to the medical domain for
the first time, demonstrating their effectiveness in cell segmentation. (2) We
analyze the redundancy of previous methods and design a new segmentation
decoder based on large convolution kernels. It achieves higher performance
while significantly reducing the number of parameters. We evaluate our method
on the most challenging benchmark and achieve state-of-the-art results (0.5080
mPQ) in cell nuclei instance segmentation with only 21.6% FLOPs compared with
the previous leading method. Our source code and models are available at
https://github.com/hustvl/LKCell.
