University of Science - VNUHCM, Ho Chi Minh City, Viet Nam; University of Social Sciences and Humanities - VNUHCM, Ho Chi Minh City, Viet Nam; Vietnam National University, Ho Chi Minh City, Viet Nam.
University of Science - VNUHCM, Ho Chi Minh City, Viet Nam; John von Neumann Institute - VNUHCM, Ho Chi Minh City, Viet Nam; Vietnam National University, Ho Chi Minh City, Viet Nam.
Comput Methods Programs Biomed. 2023 Nov;241:107748. doi: 10.1016/j.cmpb.2023.107748. Epub 2023 Aug 8.
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.
肺部结节检测和分割是分析胸部计算机断层扫描（Chest CT）以检测肺癌迹象的两个主要任务，从而提供早期治疗措施以降低死亡率。尽管有许多方法可以减少假阳性以获得有效的检测结果，但区分肺结节和背景区域仍然具有挑战性，因为它们的生物学特征相似且大小不一。我们的工作目的是提出一种通过增强肺结节的特征信息来自动检测和分割 Chest CT 中结节的方法。
我们提出了一种新的基于 UNet 的骨干网络，具有多分支注意力辅助学习机制，其中包含三个新颖的模块，即投影模块、快速级联上下文模块和边界增强模块，以进一步增强结节的特征表示。在此基础上，我们构建了 MANet，这是一种肺结节定位网络，可同时检测和分割精确的结节位置。此外，我们的 MANet 包含提案细化步骤，该步骤细化初始生成的提案，以有效减少假阳性，从而提高分割质量。
在 LUNA16 和 LIDC-IDRI 两个基准的组合上进行的综合实验表明，我们提出的模型在结节检测和分割任务方面在 FROC、IoU 和 DSC 指标上优于最先进的方法。我们的方法在肺结节检测中报告的平均 FROC 得分为 88.11%。对于肺结节分割，结果达到平均 IoU 得分为 71.29%和 DSC 得分为 82.74%。消融研究还表明，新模块的有效性可以集成到其他基于 UNet 的模型中。
与原始 UNet 设计相比，实验表明，我们的方法具有多分支注意力辅助学习能力，是一种很有前途的检测和分割肺结节实例的方法。
