FusionLungNet：用于肺CT图像分割的多尺度融合卷积与细化网络
FusionLungNet: Multi-scale Fusion Convolution with Refinement Network for Lung CT Image Segmentation
AI总结：
本文提出FusionLungNet混合网络，通过ResNet-50编码器、通道注意力、多尺度特征融合与自细化模块解决肺CT分割中的长程关系缺失和语义鸿沟问题，并发布LungSegDB数据集，取得98.04的IOU分数，显著提升分割精度。
摘要：
肺癌的早期检测至关重要，因为它能提高成功治疗的机会。自动肺图像分割有助于医生识别肺癌、COVID-19和呼吸系统疾病等疾病。然而，由于血管和支气管结构等重叠特征，以及亮度、颜色和纹理的像素级融合，肺分割具有挑战性。新的肺分割方法在识别图像成分之间的长程关系方面面临困难，依赖卷积运算可能无法捕获所有关键特征，而且肺部结构复杂。此外，特征图之间的语义鸿沟可能阻碍相关信息的整合，降低模型精度。跳跃连接也可能限制解码器获取完整信息，导致编码过程中部分信息丢失。为了克服这些挑战，我们提出了一种使用FusionLungNet网络的混合方法，该网络具有多层次结构，关键组件包括ResNet-50编码器、通道级聚合注意力（CAA）模块、多尺度特征融合（MFF）块、自细化（SR）模块和多个解码器。细化子网络使用卷积神经网络进行图像后处理以提高质量。我们的方法采用损失函数组合，包括SSIM、IOU和focal loss，以优化图像重建质量。我们创建并公开发布了一个名为LungSegDB的新肺分割数据集，包括来自LIDC-IDRI数据集（数据集版本1）的1800张CT图像和来自Kaggle的Chest CT Cancer Images数据集（数据集版本2）的700张图像。我们的方法取得了98.04的IOU分数，优于现有方法，并展示了分割精度的显著提升。https://github.com/sadjadrz/FusionLungNet
英文摘要：
Early detection of lung cancer is crucial as it increases the chances of successful treatment. Automatic lung image segmentation assists doctors in identifying diseases such as lung cancer, COVID-19, and respiratory disorders. However, lung segmentation is challenging due to overlapping features like vascular and bronchial structures, along with pixel-level fusion of brightness, color, and texture. New lung segmentation methods face difficulties in identifying long-range relationships between image components, reliance on convolution operations that may not capture all critical features, and the complex structures of the lungs. Furthermore, semantic gaps between feature maps can hinder the integration of relevant information, reducing model accuracy. Skip connections can also limit the decoder's access to complete information, resulting in partial information loss during encoding. To overcome these challenges, we propose a hybrid approach using the FusionLungNet network, which has a multi-level structure with key components, including the ResNet-50 encoder, Channel-wise Aggregation Attention (CAA) module, Multi-scale Feature Fusion (MFF) block, self refinement (SR) module, and multiple decoders. The refinement sub-network uses convolutional neural networks for image post-processing to improve quality. Our method employs a combination of loss functions, including SSIM, IOU, and focal loss, to optimize image reconstruction quality. We created and publicly released a new dataset for lung segmentation called LungSegDB, including 1800 CT images from the LIDC-IDRI dataset (dataset version 1) and 700 images from the Chest CT Cancer Images from Kaggle dataset (dataset version 2). Our method achieved an IOU score of 98.04, outperforming existing methods and demonstrating significant improvements in segmentation accuracy. https://github.com/sadjadrz/FusionLungNet
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
