SDRD-Net: A Symmetric Dual-branch Residual Dense Network for OCT and US Image Fusion.

Abstract

Ultrasound (US) images have the advantages of no radiation, high penetration, and real-time imaging, and optical coherence tomography (OCT) has the advantage of high resolution. The purpose of fusing endometrial images from optical coherence tomography (OCT) and ultrasound (US) is to combine the advantages of different modalities to ultimately obtain more complete information on endometrial thickness. To better integrate multimodal images, we first proposed a Symmetric Dual-branch Residual Dense (SDRD-Net) network for OCT and US endometrial image fusion. Firstly, using Multi-scale Residual Dense Blocks (MRDB) to extract shallow features of different modalities. Then, the Base Transformer Module (BTM) and Detail Extraction Module (DEM) are used to extract primary and advanced features. Finally, the primary and advanced features are decomposed and recombined through the Feature Fusion Module (FMM), and the fused image is output. We have conducted experiments across both private and public datasets, encompassing IVF and MIF tasks, achieving commendable results.