LKCell：使用大型卷積核進行高效的細胞核實例分割

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

對使用血液染劑血紅素和嗎啡染色的組織影像中的細胞核進行分割對於各種臨床應用和分析至關重要。由於細胞形態的複雜特徵，擁有一個大的感受野被認為對於生成高質量的分割至關重要。然而，先前的方法在實現感受野和計算負擔之間取得平衡方面面臨挑戰。為解決這個問題，我們提出了LKCell，一種高準確性和高效率的細胞分割方法。其核心見解在於發揮大型卷積核的潛力，實現計算效率高的大感受野。具體來說，（1）我們首次將預訓練的大型卷積核模型轉移到醫學領域，展示了它們在細胞分割中的有效性。 （2）我們分析了先前方法的冗餘性，並設計了一個基於大型卷積核的新分割解碼器。它在顯著減少參數數量的同時實現了更高的性能。我們在最具挑戰性的基準測試上評估了我們的方法，在細胞核實例分割中實現了最新技術水平的結果（0.5080 mPQ），與先前領先方法相比僅使用了21.6％的FLOPs。我們的源代碼和模型可在https://github.com/hustvl/LKCell 上找到。

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.