MFCNet: A multi-modal fusion and calibration networks for 3D pancreas tumor segmentation on PET-CT images

作者：Fei Wang, Chao Cheng, Weiwei Cao, Zhongyi Wu, Heng Wang, Wenting Wei, Zhuangzhi Yan, Zhaobang Liu · 发表于：Computers in Biology and Medicine · 年份：2023 · DOI：10.1016/j.compbiomed.2023.106657 · 被引用次数：42 · 研究领域：Advanced Neural Network Applications、Radiomics and Machine Learning in Medical Imaging、AI in cancer detection

In clinical diagnosis, positron emission tomography and computed tomography (PET-CT) images containing complementary information are fused. Tumor segmentation based on multi-modal PET-CT images is an important part of clinical diagnosis and treatment. However, the existing current PET-CT tumor segmentation methods mainly focus on positron emission tomography (PET) and computed tomography (CT) feature fusion, which weakens the specificity of the modality. In addition, the information interaction between different modal images is usually completed by simple addition or concatenation operations, but this has the disadvantage of introducing irrelevant information during the multi-modal semantic feature fusion, so effective features cannot be highlighted. To overcome this problem, this paper propose a novel Multi-modal Fusion and Calibration Networks (MFCNet) for tumor segmentation based on three-dimensional PET-CT images. First, a Multi-modal Fusion Down-sampling Block (MFDB) with a residual structure is developed. The proposed MFDB can fuse complementary features of multi-modal images while retaining the unique features of different modal images. Second, a Multi-modal Mutual Calibration Block (MMCB) based on the inception structure is designed. The MMCB can guide the network to focus on a tumor region by combining different branch decoding features using the attention mechanism and extracting multi-scale pathological features using a convolution kernel of different sizes. The pr...