Cardiovascular diseases are the leading cause of mortality, and accurate segmentation of ventricular regions incardiac magnetic resonance images (MRIs) is crucial for diagnosing and treating these diseases. However, fully automated and accurate right ventricle (RV) segmentation remains challenging due to the irregular cavities with ambiguous boundaries and mutably crescentic structures with relatively small targets of the RV regions in MRIs. In this article, a triple-path segmentation model, called FMMsWC, is proposed by introducing two novel image feature encoding modules, i.e., the feature multiplexing (FM) and multiscale weighted convolution (MsWC) modules, for the RV segmentation in MRIs. Considerable validation and comparative experiments were conducted on two benchmark datasets, i.e., the MICCAI2017 Automated Cardiac Diagnosis Challenge (ACDC), and the Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&MS) datasets. The FMMsWC outperforms state-of-the-art approaches, and its performance can approach that of the manual segmentation results by clinical experts, facilitating accurate cardiac index measurement for the rapid assessment of cardiac function and aiding diagnosis and treatment of cardiovascular diseases, which has great potential for clinical applications.