【摘要】 目的 结合脊柱CT和MR多模态医疗图像的互补信息，综合利用骨骼和软组织的详细特征，改善识别的准确性，提高脊柱医疗图像的分割精度，进而提供更全面的脊柱病变评估。方法 构建一个多模态医疗图像融合网络模型和一个半监督分割网络模型，分别用于脊柱CT和MR图像的融合以及基于融合图像的分割任务。多模态融合网络通过共享编码器保留不同模态的共同特征，基础编码器提取全局特征，细节编码器专注于局部细节。半监督分割网络模型采用双子网络架构，并引入对比差异评审模块和动态竞争伪标签生成模块来纠正和约束网络训练。结果 多模态融合网络在图像信息保留和特征保持方面表现优异，融合图像的高频信息噪声更少。半监督分割网络在Dice系数和Jaccard系数上均表现优异，改善了脊柱软组织与骨组织之间的清晰度。结论 多模态医疗图像融合网络和半监督分割网络有效地提升了脊柱图像的融合和分割精度。通过对比差异评审和动态竞争伪标签生成模块的引入，进一步提高分割结果的准确性，为脊柱疾病的评估提供更加清晰和可靠的图像信息。更多还原

【Abstract】 Objective By combining the complementary information from spinal CT and MR multimodal medical images, and utilizing detailed features of both bone and soft tissues, to improve the accuracy of identification and enhance the segmentation precision of spinal medical images by soft tissues, thereby providing a more comprehensive assessment of spinal lesions. Methods This paper proposed a multimodal medical image fusion network model for the fusion of spinal CT and MR images and a semi-supervised segmentation network model for the segmentation tasks based on the fused images. The multimodal fusion network retained the shared features of different modalities through a shared encoder, with a basic part extracting global features and a detail part focusing on local details. A dual-network architecture was employed to the segmentation network, which was corrected and constrained by a contrastive difference review module and a dynamic competitive pseudo-label generation module when the network was training. Results The proposed fusion network performed well in preserving image information and features, with less high-frequency noise in the fused images. The semi-supervised segmentation network excelled in both the Dice coefficient and Jaccard index, improving the clarity between spinal soft tissues and bone tissues. Conclusion The proposed multimodal medical image fusion network and semi-supervised segmentation network effectively enhance the fusion and segmentation accuracy of spinal images. The introduction of the contrastive difference review and dynamic competitive pseudo-label generation modules further improved the accuracy of the segmentation results, providing clearer and more reliable image information for the assessment of spinal diseases.更多还原