ParaU-Net: An improved UNet parallel coding network for lung nodule segmentation

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yingqi Lu , Xiangsuo Fan , Jinfeng Wang , Shaojun Chen , Jie Meng
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引用次数: 0

Abstract

Accurate segmentation of lung nodules is crucial for the early detection of lung cancer and other pulmonary diseases. Traditional segmentation methods face several challenges, such as the overlap between nodules and surrounding anatomical structures like blood vessels and bronchi, as well as the variability in nodule size and shape, which complicates the segmentation algorithms. Existing methods often inadequately address these issues, highlighting the need for a more effective solution. To address these challenges, this paper proposes an improved multi-scale parallel fusion encoding network, ParaU-Net. ParaU-Net enhances the segmentation accuracy and model performance by optimizing the encoding process, improving feature extraction, preserving down-sampling information, and expanding the receptive field. Specifically, the multi-scale parallel fusion mechanism introduced in ParaU-Net better captures the fine features of nodules and reduces interference from other structures. Experiments conducted on the LIDC (The Lung Image Database Consortium) public dataset demonstrate the excellent performance of ParaU-Net in segmentation tasks, with results showing an IoU of 87.15%, Dice of 92.16%, F1-score of 92.24%, F2-score of 92.33%, and F0.5-score of 92.69%. These results significantly outperform other advanced segmentation methods, validating the effectiveness and accuracy of the proposed model in lung nodule CT image analysis. The code is available at https://github.com/XiaoBai-Lyq/ParaU-Net.
ParaU-Net:用于肺结节分割的改进型 UNet 并行编码网络
准确分割肺结节对于早期检测肺癌和其他肺部疾病至关重要。传统的分割方法面临着一些挑战,例如结节与周围解剖结构(如血管和支气管)之间的重叠,以及结节大小和形状的可变性,这些都使分割算法变得复杂。现有方法往往无法充分解决这些问题,因此需要更有效的解决方案。为了应对这些挑战,本文提出了一种改进的多尺度并行融合编码网络 ParaU-Net。ParaU-Net 通过优化编码过程、改进特征提取、保留向下采样信息和扩大感受野来提高分割精度和模型性能。具体来说,ParaU-Net 引入的多尺度并行融合机制能更好地捕捉结节的精细特征,并减少其他结构的干扰。在 LIDC(肺部图像数据库联盟)公共数据集上进行的实验证明了 ParaU-Net 在分割任务中的卓越性能,结果显示 IoU 为 87.15%,Dice 为 92.16%,F1-score 为 92.24%,F2-score 为 92.33%,F0.5-score 为 92.69%。这些结果明显优于其他先进的分割方法,验证了所提模型在肺结节 CT 图像分析中的有效性和准确性。代码见 https://github.com/XiaoBai-Lyq/ParaU-Net。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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