Abstract
Lung cancer remains the leading cause of cancerrelated deaths globally. Accurate delineation of internal gross tumor volume (IGTV) in PET/CT imaging is pivotal for optimal radiation therapy in mobile tumors such as lung cancer to account for tumor motion, yet is hindered by the limited availability of annotated IGTV datasets and attenuated PET signal intensity at tumor boundaries. In this study, we present a transfer learningbased methodology utilizing a multimodal interactive perception network with MAMBA, pre-trained on extensive gross tumor volume (GTV) datasets and subsequently fine-tuned on a private IGTV cohort. This cohort constitutes the PET/CT subset of the Lung-cancer Unified Cross-modal Imaging Dataset (LUCID). To further address the challenge of weak PET intensities in IGTV peripheral slices, we introduce a slice interaction module (SIM) within a 2.5D segmentation framework to effectively model inter-slice relationships. Our proposed module integrates channel and spatial attention branches with depthwise convolutions, enabling more robust learning of slice-to-slice dependencies and thereby improving overall segmentation performance. A comprehensive experimental evaluation demonstrates that our approach achieves a Dice of 0.609 on the private IGTV dataset, substantially surpassing the conventional baseline score of 0.385. This work highlights the potential of transfer learning, coupled with advanced multimodal techniques and a SIM to enhance the reliability and clinical relevance of IGTV segmentation for lung cancer radiation therapy planning.
Abstract (translated)
肺癌仍然是全球癌症相关死亡的主要原因。在正电子发射断层扫描/计算机断层扫描（PET/CT）成像中精确划分内部大体肿瘤体积（IGTV），对于移动性肿瘤如肺癌的放射治疗来说至关重要，以考虑肿瘤运动的影响。然而，这一过程受限于标注好的IGTV数据集的可用性以及肿瘤边界处正电子发射信号强度的衰减。 在本研究中，我们提出了一种基于迁移学习的方法，使用多模态交互感知网络（MAMBA），该网络在广泛的原发肿瘤体积（GTV）数据集上进行了预训练，并随后在一个私有的IGTV队列上进行微调。此队列构成了肺癌统一跨模式影像数据集（LUCID）中的PET/CT子集。为了进一步解决弱PET信号强度在IGTV外围切片中带来的挑战，我们引入了一个基于2.5D分割框架的切片交互模块（SIM），以有效地建模不同切片之间的关系。我们的提议模块结合了通道和空间注意力分支以及深度卷积，从而增强了对切片间依赖性的学习能力，并提高了整体分割性能。 全面的实验评估表明，我们在私有IGTV数据集上达到了0.609的Dice系数，显著超越了传统基线分数0.385。这项工作突显了迁移学习与高级多模态技术以及SIM相结合的潜力，以增强肺癌放射治疗计划中IGTV分割的可靠性和临床相关性。
URL
https://arxiv.org/abs/2509.22841
