Related Experiment Video
Updated: Dec 2, 2025
High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging
Published on: November 10, 2015
MRI image synthesis with dual discriminator adversarial learning and difficulty-aware attention mechanism for
Baoqiang Ma1, Yan Zhao1, Yujing Yang1
1School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China.
Background And Objective:
Hippocampal subfields (HS) segmentation accuracy on high resolution (HR) MRI images is higher than that on low resolution (LR) MRI images. However, HR MRI data collection is more expensive and time-consuming. Thus, we intend to generate HR MRI images from the corresponding LR MRI images for HS segmentation.
Methods And Results:
To generate high-quality HR MRI hippocampus region images, we use a dual discriminator adversarial learning model with difficulty-aware attention mechanism in hippocampus regions (da-GAN). A local discriminator is applied in da-GAN to evaluate the visual quality of hippocampus region voxels of the synthetic images. And the difficulty-aware attention mechanism based on the local discriminator can better model the generation of hard-to-synthesis voxels in hippocampus regions. Additionally, we design a SemiDenseNet model with 3D Dense CRF postprocessing and an Unet-based model to perform HS segmentation. The experiments are implemented on Kulaga-Yoskovitz dataset. Compared with conditional generative adversarial network (c-GAN), the PSNR of generated HR T2w images acquired by our da-GAN achieves 0.406 and 0.347 improvement in left and right hippocampus regions. When using two segmentation models to segment HS, the DSC values achieved on the generated HR T1w and T2w images are both improved than that on LR T1w images.
Conclusion:
Experimental results show that da-GAN model can generate higher-quality MRI images, especially in hippocampus regions, and the generated MRI images can improve HS segmentation accuracy.
