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Open access Oct 2026

MSA-SAM: A Decoder Enhancement Framework for 3D Tumor Segmentation Based on Segment Anything

Accurate tumor segmentation in 3D medical images is essential for clinical diagnosis, treatment planning, and prognosis. However, this task remains challenging due to low contrast, irregular morphology, and indistinct boundaries. Most Segment Anything Model (SAM)-based medical imaging studies focus on improving the enc...

Yang Yan, Nan-Nan Li, Wen-Bo Huang · 0 citations
Conference Jul 2026

Research on CT to CTA image translation based on vessel-enhanced generative adversarial networks

A CT-to-CTA image translation method based on Vessel-Enhanced GAN (VE-GAN), which can generate high-quality CTA images from routine CT images without contrast agents is proposed, and ablation studies demonstrate that each innovative module effectively improves translation performance.

Nannan Li, Miao Song · 0 citations

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