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Open Access

ARTICLE

GAN-DIRNet: A Novel Deformable Image Registration Approach for Multimodal Histological Images

Haiyue Li1, Jing Xie2, Jing Ke3, Ye Yuan1, Xiaoyong Pan1, Hongyi Xin4, Hongbin Shen1,*
1 Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
2 Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
3 Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
4 Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, 200240, China
* Corresponding Author: Hongbin Shen. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.049640

Received 13 January 2024; Accepted 26 April 2024; Published online 24 June 2024

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 begin with, the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks, characterized by implicitly extracting feature descriptors of specific modalities. Additionally, modal feature description layers and registration layers collaborate in unsupervised optimization, facilitating faster convergence and more precise results. Lastly, experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database (MNIST), eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation (CRCS) dataset on the Cushing’s disease. Experimental results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency, while also exhibiting robustness across different image types.

Keywords

Histological images registration; deformable registration; generative adversarial network; cushing’s disease; machine learning; computer vision

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