6 citations · 7 across the 10 of their papers we have counts for
10 papers
Agentic Autoresearch for CT Reconstruction
Andreas Maier, Lucas Kachelriess, Siming Bayer +4
Comparing CT reconstruction methods fairly is labor-intensive and largely manual, and many benchmarks use idealized data. We ask whether a large language model (LLM) agent can do t…
WING: A Window-Prior-Based Generative Network with Gated Inception for Cross-Modality CT Synthesis
Siyuan Mei, Yan Xia, Yipeng Sun +7
Generating CT volumes from MRI and CBCT can improve treatment planning in adaptive radiotherapy while avoiding additional radiation exposure. However, direct regression of CT inten…
ScribbleDose: Scribble-Guided Dose Prediction in Radiotherapy
Zhenxi Zhang, Yitao Zhuang, Yao Pu +7
Anatomical structure masks are widely adopted in radiotherapy dose prediction, as they provide explicit geometric constraints that facilitate structure-dose coupling. However, conv…
Generating synthetic computed tomography for radiotherapy: SynthRAD2025 challenge report
Viktor Rogowski, Maarten L. Terpstra, Niklas Wahl +30
Radiation therapy (RT) requires precise dose delivery over multiple fractions, with CT fundamental for treatment planning due to its electron density information. Repeated CT acqui…
Generative Drifting for Conditional Medical Image Generation
Zirong Li, Siyuan Mei, Weiwen Wu +3
Conditional medical image generation plays an important role in many clinically relevant imaging tasks. However, existing methods still face a fundamental challenge in balancing in…
GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation
Fengming Lin, Yan Xia, Michael MacRaild +6
The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning. Confronted with significant domain shifts and class imbalance in 3D Rotat…
Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning
Fengming Lin, Yan Xia, Michael MacRaild +6
Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models. Since cros…
Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI
Nicolás Gaggion, Benjamin A. Matheson, Yan Xia +6
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volume…
Learned Local Attention Maps for Synthesising Vessel Segmentations
Yash Deo, Rodrigo Bonazzola, Haoran Dou +5
Magnetic resonance angiography (MRA) is an imaging modality for visualising blood vessels. It is useful for several diagnostic applications and for assessing the risk of adverse ev…
Adaptive Semi-Supervised Segmentation of Brain Vessels with Ambiguous Labels
Fengming Lin, Yan Xia, Nishant Ravikumar +3
Accurate segmentation of brain vessels is crucial for cerebrovascular disease diagnosis and treatment. However, existing methods face challenges in capturing small vessels and hand…