LKCell: Efficient Cell Nuclei Instance Segmentation with Large
  Convolution Kernels
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.
66a412da7666005eccfda0e0
Ziwei Cui
admin_assigned
2024-07-30T14:47:03.938Z
645268ea49b6b9a238384172
/avatars/563f5414226c7c91a8003faf966dde33.svg
ZIWEICUI
user
ZiweiCui
66a412da7666005eccfda0e1
Jingfeng Yao
66a412da7666005eccfda0e2
Lunbin Zeng
admin_assigned
2024-07-30T14:47:18.646Z
6375dfa7f9aafd41ce145254
/avatars/19e7460dd7ea6c60e1c52d5707660cc8.svg
Lunbin Zeng
user
xiazhi
66a412da7666005eccfda0e3
Juan Yang
66a412da7666005eccfda0e4
Wenyu Liu
66a412da7666005eccfda0e5
Xinggang Wang
admin_assigned
2024-07-30T14:48:01.645Z
62600de6d47e3dbae32ce1ce
/avatars/a536417cfec6e10ac415091bd1829426.svg
Xinggang Wang
user
xinggangw
LKCell: Efficient Cell Nuclei Instance Segmentation with Large
  Convolution Kernels
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.
LKCell uses large convolution kernels to achieve high accuracy and efficiency in cell nuclei segmentation with reduced computational cost.
large convolution kernels cell nuclei segmentation medical domain segmentation decoder mPQ FLOPs