Cardiac Magnetic Resonance Image Segmentation Method Based on Multi-Scale Feature Fusion and Sequence Relationship Learning.

Accurate segmentation of the left atrial structure using magnetic resonance images provides an important basis for the diagnosis of atrial fibrillation (AF) and its treatment using robotic surgery. In this study, an image segmentation method based on sequence relationship learning and multi-scale feature fusion is proposed for 3D to 2D sequence conversion in cardiac magnetic resonance images and the varying scales of left atrial structures within different slices. Firstly, a convolutional neural network layer with an attention module was designed to extract and fuse contextual information at different scales in the image, to strengthen the target features using the correlation between features in different regions within the image, and to improve the network's ability to distinguish the left atrial structure.

Design and construction of polymerized-chitosan coated Fe3O4 magnetic nanoparticles and its application for hydrophobic drug delivery.

In this study, a novel hydrogel, chitosan (CS) crosslinked carboxymethyl-β-cyclodextrin (CM-β-CD) polymer modified Fe3O4 magnetic nanoparticles was synthesized for delivering hydrophobic anticancer drug 5-fluorouracil (CS-CDpoly-MNPs). Carboxymethyl-β-cyclodextrin being grafted on the Fe3O4 nanoparticles (CDpoly-MNPs) contributed to an enhancement of adsorption capacities because of the inclusion abilities of its hydrophobic cavity with insoluble anticancer drugs through host-guest interactions. Experimental results indicated that the amounts of crosslinking agent and bonding times played a crucial role in determining morphology features of the hybrid nanocarriers.