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基于多空间信息提取的级联分块配准模型

Cascaded Block Registration Model Based on Multi-spatial Information Extraction
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摘要 在基于深度学习的医学图像配准中,当医学图像中包含多种组织类型时,不同组织之间结构的不同可能会导致网络配准的精度下降,特别是在复杂形变区域,如组织的交界处和病变区域,精准的配准变得更加困难,现有的配准算法对复杂形变区域的关注度不高,导致配准精度较低.同时现有的配准网络无法同时对图像的局部和全局空间信息进行捕获,导致网络的鲁棒性不够,在迁移到其他器官的配准工作中时配准准确率低.为了解决上述的问题,本文提出一种基于多空间信息提取的级联分块配准模型,本模型可以有效利用输入图像的局部和空间信息,并通过分块融合的技术,将医学图像进行分块并依次对每个图像进行精细配准生成相应的形变场块,在模型的最后阶段将生成的形变场块进行融合还原,以增强网络对局部复杂形变区域的配准强度.实验结果表明,所提方法不仅在脑部配准上有所提升,并且在其他人体部位的配准中也有较好的表现,提高了医学图像配准的准确性和可靠性,为临床医生提供更好的诊断和治疗支持. In medical image registration based on deep learning,when the medical image contains multiple tissue types,the structural difference between different tissue may lead to a decrease in the accuracy of network registration,especially in complex deformation regions,such as the junction of tissue and the lesion region,and accurate registration becomes more difficult.The existing registration algorithms have low registration accuracy for complex deformation regions.At the same time,the existing registration network cannot capture the local and global spatial information of the image at the same time,resulting in insufficient robustness of the network and low accuracy when it is transferred to other organs for registration.In order to solve the above problems,this study creates a cascaded block registration model based on multi-spatial information extraction.This model can effectively use the local and spatial information of input images,divide medical images into blocks through block fusion technology,and perform fine registration for each image in turn to generate corresponding deformation field blocks.In the last stage of the model,the generated deformation field blocks are fused and restored to enhance the registration strength of the network for the local complex deformation region.The experimental results show that the proposed method not only improves the registration of the brain but also performs well in the registration of other human body parts,which improves the accuracy and reliability of medical image registration and provides better diagnosis and treatment support for clinicians.
作者 王南南 程远志 WANG Nan-Nan;CHENG Yuan-Zhi(School of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266061,China;School of Information Science and Engineering,Harbin Institute of Technology,Weihai,Weihai 264209,China)
出处 《计算机系统应用》 2024年第2期125-133,共9页 Computer Systems & Applications
关键词 医学影像 级联网络 自注意力机制 分块融合 信息提取 注意力机制 图像融合 medical image cascading network self-attention mechanism block fusion information extraction attention mechanism image fusion
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