Abstract
BACKGROUND: Accurate and automated segmentation of pancreatic tumors from CT images via deep learning is essential for the clinical diagnosis of pancreatic cancer. However, two key challenges persist: (a) complex phenotypic variations in pancreatic morphology cause segmentation models to focus predominantly on healthy tissue over tumors, compromising tumor feature extraction and segmentation accuracy; (b) existing methods often struggle to retain fine-grained local features, leading to performance degradation in pancreas-tumor segmentation. METHODS: To overcome these limitations, we propose SMF-Net (Semantic-Guided Multimodal Fusion Network), a novel multimodal medical image segmentation framework integrating a CNN-Transformer hybrid encoder. The framework incorporates AMBERT, a progressive feature extraction module, and the Multimodal Token Transformer (MTT) to fuse visual and semantic features for enhanced tumor localization. Additionally, The Multimodal Enhanced Attention Module (MEAM) further improves the retention of local discriminative features. To address multimodal data scarcity, we adopt a semi-supervised learning paradigm based on a Dual-Adversarial-Student Network (DAS-Net). Furthermore, in collaboration with Zhuzhou Central Hospital, we constructed the Multimodal Pancreatic Tumor Dataset (MPTD). RESULTS: The experimental results on the MPTD indicate that our model achieved Dice scores of 79.25% and 64.21% for pancreas and tumor segmentation, respectively, showing improvements of 2.24% and 4.18% over the original model. Furthermore, the model outperformed existing state-of-the-art methods on the QaTa-COVID-19 and MosMedData lung infection segmentation datasets in terms of average Dice scores, demonstrating its strong generalization ability. CONCLUSION: The experimental results demonstrate that SMF-Net delivers accurate segmentation of both pancreatic, tumor and pulmonary regions, highlighting its strong potential for real-world clinical applications.