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BiM-GeoAttn-Net: Linear-Time Depth Modeling with Geometry-Aware Attention for 3D Aortic Dissection CTA Segmentation
Yuan Zhang, Lei Liu, Jialin Zhang +3
Accurate segmentation of aortic dissection (AD) lumens in CT angiography (CTA) is essential for quantitative morphological assessment and clinical decision-making. However, reliabl…
FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Nan Mu, Ruiqi Song, Xiaoning Li +3
Background: Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. Precise segmentation of coronary arteries from invasive coronary angiography (IC…
Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation
Hong Zheng, Yucheng Chen, Nan Mu +1
Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based…
Parameter-Efficient Transformer with Hybrid Axial-Attention for Medical Image Segmentation
Yiyue Hu, Lei Zhang, Nan Mu +1
Transformers have achieved remarkable success in medical image analysis owing to their powerful capability to use flexible self-attention mechanism. However, due to lacking intrins…