TransFusion-net for multifocus microscopic biomedical image fusion.

Background And Objective: Due to the depth of focus (DOF) limitations of the optical systems of microscopes, it is often difficult to achieve full clarity from microscopic biomedical images under high-magnification microscopy. Multifocus microscopic biomedical image fusion (MFBIF) can effectively solve this problem. Considering both information richness and visual authenticity, this paper proposes a transformer network for MFBIF called TransFusion-Net.

[Infrared Imaging Meibomian Gland Segmentation System Based on Deep Learning].

In order to better assist doctors in the diagnosis of dry eye and improve the ability of ophthalmologists to recognize the condition of meibomian gland, a meibomian gland image segmentation and enhancement method based on Mobile-U-Net network was proposed. Firstly, Mobile-Net is used as the coding part of U-Net for down sampling, and then features are extracted and fused with the features in decoder to guide image segmentation. Secondly, the segmentation of meibomian gland region is enhanced to assist doctors to judge the condition.