title: LKCell: Efficient Cell Nuclei Instance Segmentation with Large
    Convolution Kernels
summary: 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.
paper_authors._id: 66a412da7666005eccfda0e0
paper_authors.name: Ziwei Cui
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-30T14:47:03.938Z
paper_authors._id: 645268ea49b6b9a238384172
paper_authors.avatarUrl: /avatars/563f5414226c7c91a8003faf966dde33.svg
paper_authors.fullname: ZIWEICUI
paper_authors.type: user
paper_authors.user: ZiweiCui
paper_authors._id: 66a412da7666005eccfda0e1
paper_authors.name: Jingfeng Yao
paper_authors._id: 66a412da7666005eccfda0e2
paper_authors.name: Lunbin Zeng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-30T14:47:18.646Z
paper_authors._id: 6375dfa7f9aafd41ce145254
paper_authors.avatarUrl: /avatars/19e7460dd7ea6c60e1c52d5707660cc8.svg
paper_authors.fullname: Lunbin Zeng
paper_authors.type: user
paper_authors.user: xiazhi
paper_authors._id: 66a412da7666005eccfda0e3
paper_authors.name: Juan Yang
paper_authors._id: 66a412da7666005eccfda0e4
paper_authors.name: Wenyu Liu
paper_authors._id: 66a412da7666005eccfda0e5
paper_authors.name: Xinggang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-30T14:48:01.645Z
paper_authors._id: 62600de6d47e3dbae32ce1ce
paper_authors.avatarUrl: /avatars/a536417cfec6e10ac415091bd1829426.svg
paper_authors.fullname: Xinggang Wang
paper_authors.type: user
paper_authors.user: xinggangw
paper_title: LKCell: Efficient Cell Nuclei Instance Segmentation with Large
    Convolution Kernels
paper_summary: 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.
paper_ai_keywords: convolution kernels pre-trained large convolution kernel models receptive fields FLOPs mPQ cell segmentation instance segmentation