MVRNet: A Multi-View Refinement Network for Accurate Recognition of Challenging Intracranial Aneurysms in Enhanced 3D CTA Images

REC-UNet: a 2D U-Net model with residual cross-dimensional attention for liver tumor segmentation

A novel residual “Enhancement-Calibration” U-Net architecture, termed REC-UNet, which achieves high overall segmentation accuracy across diverse lesion sizes and contrast conditions without relying on explicit size-stratified optimization.

MRD-UNet: a compute-efficient medical image segmentation architecture with multi-attention and parallel dilated fusion

MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.

A neighborhood attention transformer network for enhanced 3D segmentation of the left anterior descending artery.

NA-UNETR effectively balances local precision and global context, enabling accurate segmentation of thin, low-contrast LAD structures and demonstrates a robust and computationally efficient framework for substructure-level cardiac segmentation in radiotherapy planning.

Current Medical Imaging

A 2.5D Multimodal Approach for Brain Tumor Segmentation with Improved Robustness to Incomplete MRI Inputs

The problem of accurate identification of brain tumors using multi-modal MRIs still poses significant challenges due to tumor heterogeneity and variations across different image modes, as well as inconsistent availability of imaging modalities in real-world applications. Although deep learning algorithms such as convol...

Attention-Enhanced Bimodal 3D Medical Image Segmentation with Two-Stage Learning

This work proposes an enhanced 3D segmentation framework, UAtten-Unetr, designed to improve segmentation accuracy and robustness in complex medical scenarios, and innovatively developed a unified loss function based on bimodal modality-specific Dice constraints and uncertainty regularization, optimized for synchronous...