LKCell: Efficient Cell Nuclei Instance Segmentation with Large Convolution Kernels

• URL: http://arxiv.org/abs/2407.18054v1
• Date: Thu, 25 Jul 2024 14:07:49 GMT
• Title: LKCell: Efficient Cell Nuclei Instance Segmentation with Large Convolution Kernels
• Authors: Ziwei Cui, Jingfeng Yao, Lunbin Zeng, Juan Yang, Wenyu Liu, Xinggang Wang,
• Abstract summary: 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. We analyze the redundancy of previous methods and design a new segmentation decoder based on large convolution kernels.
• Score: 32.157968641130545
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• CausalCellSegmenter: Causal Inference inspired Diversified Aggregation Convolution for Pathology Image Segmentation [9.021612471640635]
  Deep learning models have shown promising performance for cell nucleus segmentation in the field of pathology image analysis. We propose a novel framework termed CausalCellSegmenter, which combines Causal Inference Module (CIM) with Diversified Aggregation Convolution (DAC) techniques.
  arXiv  Detail & Related papers  (2024-03-10T03:04:13Z)
• Cell Graph Transformer for Nuclei Classification [78.47566396839628]
  We develop a cell graph transformer (CGT) that treats nodes and edges as input tokens to enable learnable adjacency and information exchange among all nodes. Poorly features can lead to noisy self-attention scores and inferior convergence. We propose a novel topology-aware pretraining method that leverages a graph convolutional network (GCN) to learn a feature extractor.
  arXiv  Detail & Related papers  (2024-02-20T12:01:30Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• A Novel Dataset and a Deep Learning Method for Mitosis Nuclei Segmentation and Classification [10.960222475663006]
  Mitosis nuclei count is one of the important indicators for the pathological diagnosis of breast cancer. We propose a two-stage mitosis segmentation and classification method, named SCMitosis. The proposed model is verified on the ICPR 2012 dataset, and the highest F-score value of 0.8687 is obtained.
  arXiv  Detail & Related papers  (2022-12-27T08:12:42Z)
• MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy [9.405458160620533]
  We propose MEDIAR, a holistic pipeline for cell instance segmentation under multi-modality. We achieve a 0.9067 F1-score at the validation phase while satisfying the time budget. To facilitate subsequent research, we provide the source code and trained model as open-source.
  arXiv  Detail & Related papers  (2022-12-07T05:09:24Z)
• Efficient Approximate Kernel Based Spike Sequence Classification [56.2938724367661]
  Machine learning models, such as SVM, require a definition of distance/similarity between pairs of sequences. Exact methods yield better classification performance, but they pose high computational costs. We propose a series of ways to improve the performance of the approximate kernel in order to enhance its predictive performance.
  arXiv  Detail & Related papers  (2022-09-11T22:44:19Z)
• EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring [4.555723508665994]
  We introduce a small convolutional neural network (CNN) for volumetric cell segmentation. Our model is efficient and has an asymmetric encoder-decoder structure with very few parameters in the decoder. Our method achieves top-ranking results, while our CNN model has an up to 25x lower number of parameters than other top-ranking methods.
  arXiv  Detail & Related papers  (2022-04-06T18:02:15Z)
• Kernel Identification Through Transformers [54.3795894579111]
  Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models. This work addresses the challenge of constructing custom kernel functions for high-dimensional GP regression models. We introduce a novel approach named KITT: Kernel Identification Through Transformers.
  arXiv  Detail & Related papers  (2021-06-15T14:32:38Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation [71.50526869670716]
  We propose a two-step post-processing procedure, Split and Expand, to improve the conversion of segmentation maps to instances. In the Split step, we split clumps of cells from the segmentation map into individual cell instances with the guidance of cell-center predictions. In the Expand step, we find missing small cells using the cell-center predictions.
  arXiv  Detail & Related papers  (2020-07-21T14:05:09Z)
• Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection [6.836162272841265]
  We introduce a novel supervised technique for cell segmentation in a multi-task learning paradigm. A combination of a multi-task loss, based on the region and cell boundary detection, is employed for an improved prediction efficiency of the network. We observe an overall Dice score of 0.93 on the validation set, which is an improvement of over 15.9% on a recent unsupervised method, and outperforms the popular supervised U-net algorithm by at least $5.8%$ on average.
  arXiv  Detail & Related papers  (2020-05-19T11:31:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.