Lung tumor segmentation in medical imaging is a critical step in the diagnosis and treatment planning for lung cancer. Accurate segmentation, however, is challenging due to the variability in tumor size, shape, and contrast against surrounding tissues. In this work, we present MSMV-Net, a novel deep learning architecture that integrates multi-scale multi-view (MSMV) learning modules and multi-scale uncertainty-based deep supervision (MUDS) for enhanced segmentation of lung tumors in computed tomography images. MSMV-Net capitalizes on the strengths of multi-view analysis and multi-scale feature extraction to address the limitations posed by small 3D lung tumors. The results indicate that MSMV-Net achieves state-of-the-art performance in lung tumor segmentation, recording a global Dice score of 55.60% on the LUNA dataset and 59.94% on the MSD dataset. Ablation studies conducted on the MSD dataset further validate that our method enhances segmentation accuracy.

中文翻译：

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用于肺肿瘤分割的多尺度和多视图网络

医学成像中的肺肿瘤分割是肺癌诊断和治疗计划的关键步骤。然而，由于肿瘤大小、形状以及与周围组织的对比度的变化，准确分割具有挑战性。在这项工作中，我们提出了 MSMV-Net，这是一种新颖的深度学习架构，它集成了多尺度多视图（MSMV）学习模块和多尺度基于不确定性的深度监督（MUDS），用于增强计算机断层扫描图像中肺部肿瘤的分割。 MSMV-Net 利用多视图分析和多尺度特征提取的优势来解决小型 3D 肺部肿瘤带来的局限性。结果表明，MSMV-Net 在肺肿瘤分割方面实现了最先进的性能，在 LUNA 数据集上的全局 Dice 得分为 55.60%，在 MSD 数据集上的全局 Dice 得分为 59.94%。在 MSD 数据集上进行的消融研究进一步验证了我们的方法提高了分割准确性。