arXiv 2412.063142025-04-30eess.IVcs.AIcs.CV

CAD-Unet：一种用于从CT图像中准确分割COVID-19肺部感染的胶囊网络增强Unet架构

CAD-Unet: A Capsule Network-Enhanced Unet Architecture for Accurate Segmentation of COVID-19 Lung Infections from CT Images

School of Information Engineering, Ningxia University（宁夏大学信息工程学院）；Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, Ningxia University（宁夏人工智能与信息安全重点实验室）；School of Mathematics and Computer Science, Ningxia Normal University（宁夏师范学院数学与计算机科学学院）；School of Advanced Interdisciplinary Studies, Ningxia University（宁夏大学先进交叉学科学院）

Yijie Dang, Weijun Ma, Xiaohu Luo, Huaizhu Wang

备注：: Published in Medical Image Analysis, Volume 103, 2025, Pages 103583. DOI: 10.1016/j.media.2025.103583 This is the author's pre-print version prior to final journal edits. Final published version available at: https://www.sciencedirect.com/science/article/pii/S1361841525001306

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AI总结：

本文提出CAD-Unet深度网络架构，通过将胶囊网络融入Unet框架并设计耦合路径，高效融合两者优势以提取复杂病灶空间信息，在四个公开数据集上实现了对COVID-19肺部感染优越的分割性能。

摘要：

自2019年COVID-19大流行爆发以来，医学影像已成为诊断COVID-19肺炎的主要模态。在临床环境中，从计算机断层扫描图像中分割肺部感染能够实现COVID-19的快速准确量化与诊断。肺部COVID-19感染的分割面临巨大挑战，主要原因是磨玻璃影表现所呈现的边界模糊和对比度有限。此外，浸润物、肺组织和肺壁之间的混淆相似性进一步使该分割任务复杂化。为了应对这些挑战，本文提出了一种名为CAD-Unet的新型深度网络架构，用于分割COVID-19肺部感染。在该架构中，将胶囊网络结合到现有的Unet框架中。胶囊网络代表了一种不同于传统卷积神经网络的新型网络架构。它们利用向量在胶囊之间传递信息，促进复杂病灶空间信息的提取。此外，我们设计了一条胶囊编码器路径，并在Unet编码器和胶囊编码器之间建立了一条耦合路径。该设计最大化了两种网络结构的互补优势，同时实现了高效的信息融合。最后，在四个公开可用的数据集上进行了大量实验，涵盖二分类分割任务和多分类分割任务。实验结果表明，所提出的模型具有优越的分割性能。代码已发布于：https://github.com/AmanoTooko-jie/CAD-Unet。

英文摘要：

Since the outbreak of the COVID-19 pandemic in 2019, medical imaging has emerged as a primary modality for diagnosing COVID-19 pneumonia. In clinical settings, the segmentation of lung infections from computed tomography images enables rapid and accurate quantification and diagnosis of COVID-19. Segmentation of COVID-19 infections in the lungs poses a formidable challenge, primarily due to the indistinct boundaries and limited contrast presented by ground glass opacity manifestations. Moreover, the confounding similarity between infiltrates, lung tissues, and lung walls further complicates this segmentation task. To address these challenges, this paper introduces a novel deep network architecture, called CAD-Unet, for segmenting COVID-19 lung infections. In this architecture, capsule networks are incorporated into the existing Unet framework. Capsule networks represent a novel network architecture that differs from traditional convolutional neural networks. They utilize vectors for information transfer among capsules, facilitating the extraction of intricate lesion spatial information. Additionally, we design a capsule encoder path and establish a coupling path between the unet encoder and the capsule encoder. This design maximizes the complementary advantages of both network structures while achieving efficient information fusion. \noindent Finally, extensive experiments are conducted on four publicly available datasets, encompassing binary segmentation tasks and multi-class segmentation tasks. The experimental results demonstrate the superior segmentation performance of the proposed model. The code has been released at: https://github.com/AmanoTooko-jie/CAD-Unet.

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