DERE-Net: A dual-encoder residual enhanced U-Net for muscle fiber segmentation of H&E images

Abstract

Accurate segmentation of hematoxylin-eosin (H&E) muscle fiber images is crucial for the diagnosis of weightless muscle atrophy. However, uneven contrast, blurred fiber boundaries and adhesions in H&E images are still challenges for accurate segment complete muscle fiber. Although the existing single-encoder based U-shaped networks can extract local information about the target in H&E images, ignores remote dependencies between muscle fibers and low-level detail features, resulting in poor segmentation accuracy when facing H&E images with complex contrast. Therefore, we propose a dual-encoder residual enhanced U-Net segmentation model (DERE-Net) for effective segment muscle fibers in H&E images. DERE-Net uses Transformer and residual CNN to extract global context information and local features of muscle fibers in parallel, and fuses these two features to enable the model to accurately identify the muscle fibers. The multi-scale semantic information fusion (MSIF) module also utilizes the differences between multi-scale features to recover undetected muscle fiber, thus improving the network’s ability to recognize the object regions. In addition, the attention module is added to the skip connection, making the muscle fiber regions information more prominent and improving the robustness of the model. To demonstrate the DERE-Net effect, the comparative experiments are conducted on our own muscle fiber H&E image dataset, GLAS and MoNuSeg. DERE-Net achieved excellent performance on the muscle fiber H&E dataset (DSC 0.919), GLAS dataset (DSC 0.912), and MoNuSeg dataset (DSC 0.821). These results indicate that DERE-Net can accurately predict muscle fiber regions, providing a new method for accurate diagnosis of muscle atrophy in the future.

Jimin Liang

Professor of Electronic Engineering

My research interests include artificial intelligence and computer vision.