HSV-Net: hybrid state-space and self-supervised vision fusion with progressive contour learning for CT pulmonary nodule segmentation.

Authors

Liang Cheng, Xinsheng Wan

Published in

Frontiers in oncology. Volume 16. Pages 1964406. Epub Oct 06, 2026.

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 after nodule localization: an SSM3D stream provides whole-patch context with linear-complexity state-space blocks, and an OrthoLoRA stream adapts frozen DINOv2 features on axial, coronal, and sagittal slices. A Hybrid Gated Mixer (HGM) fuses the two streams before FPN decoding, and Progressive Contour Learning (PCL) supervises overlap, signed-distance-field geometry, and uncertainty-weighted contour refinement. On a 10-patient QIN Lung CT/QIN-LungCT-Seg development subset (n = 12 tumors; nested patient-level 5-fold CV), HSV-Net obtained the highest mean Dice (90.1% ± 1.0%; 95% CI [88.9, 91.3]) and Boundary F1 (86.3% ± 1.3%) among the compared methods on 128 × 128 × 64 patches, with a latency of 58 ms. A standard full-volume nnU-Net v2 baseline reached 88.6% ± 1.0% Dice on the same QIN folds, still below HSV-Net. On an independent LIDC-IDRI benchmark with expert volumetric consensus masks (186 patients/294 nodules; patient-level 5-fold CV within LIDC, not QIN→LIDC transfer), HSV-Net achieved 87.8% ± 1.1% Dice and 83.6% ± 1.3% Boundary F1, outperforming full-volume nnU-Net (87.1% ± 1.0% Dice). LUNA16 was retained only as a proxy size/localization check with diameter-matched spherical references, not as contour external validation.

PMID:
42903127
Bibliographic data and abstract were imported from PubMed on 22 Oct 2026.

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