LKCell：使用大卷积核进行高效细胞核实例分割

LKCell: Efficient Cell Nuclei Instance Segmentation with Large Convolution Kernels

July 25, 2024

作者: Ziwei Cui, Jingfeng Yao, Lunbin Zeng, Juan Yang, Wenyu Liu, Xinggang Wang

cs.AI

摘要

在用血液染料伊红和噻吩（H&E）染色的组织图像中细胞核的分割对于各种临床应用和分析至关重要。由于细胞形态的复杂特征，一个大的感受野被认为是生成高质量分割的关键。然而，先前的方法在实现感受野和计算负担之间的平衡方面面临挑战。为解决这一问题，我们提出了LKCell，一种高准确性和高效率的细胞分割方法。其核心洞察力在于释放大卷积核的潜力，实现计算效率高的大感受野。具体来说，（1）我们首次将预训练的大卷积核模型转移到医学领域，证明它们在细胞分割中的有效性。 （2）我们分析了先前方法的冗余性，并设计了一个基于大卷积核的新分割解码器。它在显著减少参数数量的同时实现了更高的性能。我们在最具挑战性的基准测试上评估了我们的方法，并在细胞核实例分割中取得了最先进的结果（0.5080 mPQ），与先前领先方法相比，FLOPs仅为21.6％。我们的源代码和模型可在https://github.com/hustvl/LKCell 上获得。

English

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.