ArticleFrontiers in oncology2026
HSV-Net: hybrid state-space and self-supervised vision fusion with progressive contour learning for CT pulmonary nodule segmentation.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate voxel-level segmentation of pulmonary nodules on chest CT is needed for diameter and volume measurement, growth tracking, and quantitative assessment in lung cancer screening. The task remains difficult: nodules vary in attenuation, size, and contact with vessels or pleura, and clinically relevant morphology often lies in a thin, ambiguous margin. Existing systems commonly trade contour fidelity for speed-large prompt-driven foundation models can be accurate but slow, whereas compact CNN or SSM decoders are efficient yet tend to smooth spiculated and ground-glass borders. We propose HSV-Net (Hybrid State-space-Vision Network), a dual-stream 3D network that is prompt-free
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