目前，在分析胸部计算机断层扫描（Chest CT）以便检测肺癌前兆、提供早期治疗措施以减少死亡率的过程中，肺结节的检测和分割是两个主要任务。尽管已经提出了许多方法来减少误报以获得有效的检测结果，但由于肺结节和背景区域的生物特征相似且大小各异，区分它们仍然具有挑战性。我们的工作目的是提出一种自动检测和分割胸部CT中肺结节的方法，通过增强肺结节的特征信息。我们提出了一种基于UNet的新型主干网络，具有多支路注意力辅助学习机制，其中包括三个新模块，即投影模块、快速级联上下文模块和边界增强模块，以进一步增强肺结节的特征表示。在此基础上，我们建立了MANet，一种同时检测和分割精确结节位置的肺结节定位网络。此外，我们的MANet还包含一个提案细化步骤，用于有效减少误报并产生良好的分割质量。基于LUNA16和LIDC-IDRI两个基准集的综合实验表明，我们提出的模型在肺结节检测和分割任务方面优于现有方法，评价指标包括FROC、IoU和DSC。我们的方法在肺结节检测方面报告了88.11%的平均FROC分数。在肺结节分割方面，结果达到了71.29%的平均IoU分数和82.74%的DSC分数。消融实验还表明了新模块的有效性，这些模块可以集成到其他基于UNet的模型中。实验证明，与原始UNet设计相比，具有多支路注意力辅助学习能力的我们的方法是一种有前景的检测和分割肺结节实例的方法。版权所有©2023年 Elsevier B.V. 发表。

Pulmonary nodule detection and segmentation are currently two primary tasks in analyzing chest computed tomography (Chest CT) in order to detect signs of lung cancer, thereby providing early treatment measures to reduce mortality. Even though there are many proposed methods to reduce false positives for obtaining effective detection results, distinguishing between the pulmonary nodule and background region remains challenging because their biological characteristics are similar and varied in size. The purpose of our work is to propose a method for automatic nodule detection and segmentation in Chest CT by enhancing the feature information of pulmonary nodules.We propose a new UNet-based backbone with multi-branch attention auxiliary learning mechanism, which contains three novel modules, namely, Projection module, Fast Cascading Context module, and Boundary Enhancement module, to further enhance the nodule feature representation. Based on that, we build MANet, a lung nodule localization network that simultaneously detects and segments precise nodule positions. Furthermore, our MANet contains a Proposal Refinement step which refines initially generated proposals to effectively reduce false positives and thereby produce the segmentation quality.Comprehensive experiments on the combination of two benchmarks LUNA16 and LIDC-IDRI show that our proposed model outperforms state-of-the-art methods in the tasks of nodule detection and segmentation tasks in terms of FROC, IoU, and DSC metrics. Our method reports an average FROC score of 88.11% in lung nodule detection. For the lung nodule segmentation, the results reach an average IoU score of 71.29% and a DSC score of 82.74%. The ablation study also shows the effectiveness of the new modules which can be integrated into other UNet-based models.The experiments demonstrated our method with multi-branch attention auxiliary learning ability are a promising approach for detecting and segmenting the pulmonary nodule instances compared to the original UNet design.Copyright © 2023. Published by Elsevier B.V.