{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T04:53:54Z","timestamp":1791262434384,"version":"4.1.0"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>\n                    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\u2014large 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\u2013Vision Network), a dual-stream 3D network that is prompt-free\n                    <jats:italic>after<\/jats:italic>\n                    nodule localization: an\n                    <jats:italic>SSM3D<\/jats:italic>\n                    stream provides whole-patch context with linear-complexity state-space blocks, and an\n                    <jats:italic>OrthoLoRA<\/jats:italic>\n                    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                    <jats:italic>n<\/jats:italic>\n                    =\u00a012 tumors; nested patient-level 5-fold CV), HSV-Net obtained the highest mean Dice (90.1% \u00b1 1.0%; 95% CI [88.9, 91.3]) and Boundary F1 (86.3% \u00b1 1.3%) among the compared methods on 128 \u00d7 128 \u00d7 64 patches, with a latency of 58\u00a0ms. A standard full-volume nnU-Net v2 baseline reached 88.6% \u00b1 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\u2192LIDC transfer), HSV-Net achieved 87.8% \u00b1 1.1% Dice and 83.6% \u00b1 1.3% Boundary F1, outperforming full-volume nnU-Net (87.1% \u00b1 1.0% Dice). LUNA16 was retained only as a proxy size\/localization check with diameter-matched spherical references, not as contour external validation.\n                  <\/jats:p>","DOI":"10.3389\/fonc.2026.1964406","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T04:25:25Z","timestamp":1791260725000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["HSV-Net: hybrid state-space and self-supervised vision fusion with progressive contour learning for CT pulmonary nodule segmentation"],"prefix":"10.3389","volume":"16","author":[{"given":"Liang","family":"Cheng","sequence":"first","affiliation":[{"name":"Dexing People\u2019s Hospital","place":["Dexing, Jiangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinsheng","family":"Wan","sequence":"additional","affiliation":[{"name":"Dexing People\u2019s Hospital","place":["Dexing, Jiangxi, 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