Haiyue Li1, Jing Xie2, Jing Ke3, Ye Yuan1, Xiaoyong Pan1, Hongyi Xin4, Hongbin Shen1,*
CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 487-506, 2024, DOI:10.32604/cmc.2024.049640
Abstract Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue. Convolutional neural network (CNN) and generative adversarial network (GAN) are pivotal in medical image registration. However, existing methods often struggle with severe interference and deformation, as seen in histological images of conditions like Cushing’s disease. We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator in GAN. In this study, we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration. To… More >
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