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PYCAD BLOG

Insights on Medical Imaging, AI & DICOM

Guides, project breakdowns, and lessons learned from building medical imaging platforms — written by the PYCAD team.

Four panels from Fig. 4: raw XCA input, DSA, DOSTA-Net, and TDBM vessel map on the same internal coronary angiography frame (Hao et al., arXiv:2610.14105)

TDBM: Vessel Maps from X-ray Coronary Angiography

TDBM, by Hao, Cantrell, Abdalla, Ansari and Zhou (arXiv:2610.14105), recovers contrast-preserving coronary vessels from X-ray angiography sequences with a temporal diffusion bridge trained on physics-informed synthetic data, without manual vessel labels. Code is GPL-3.0 on GitHub with Google Drive checkpoints.

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Four coronal panels from Fig. 4: FreeSurfer WM/GM on 1 mm T1w, FreeSurfer N/A on 1x1x5 mm FLAIR, BrainNet synth-random surfaces on the same FLAIR, and RAC on that FLAIR (Nielsen et al., arXiv:2610.16595)

BrainNet: Cortical Surfaces from Any Brain MRI

BrainNet, by Nielsen, Gopinath, Hoopes, Dalca, Thielscher, Iglesias and Puonti (arXiv:2610.16595), reconstructs WM and GM cortical surfaces with vertex-wise uncertainty and fsaverage spherical registration from heterogeneous brain MRI, including thick-slice clinical FLAIR. Inference is about 0.6 s on GPU. Code is GPL-3.0 on GitHub with three TopoFit weight variants.

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Coronal MIPs of SCR case pet_ct_bc_eh_Anonymous_0004 showing thoracic CT, Pass-1 random-forest probability map, hybrid SCF plus U-Net fusion, and dilated ground-truth rib centerlines (Quan Wang, arXiv:2610.16047, Fig. 3 panels a, b, e, f)

ribline: Numbered 3D Rib Centerlines from Thoracic CT

ribline, by Quan Wang (arXiv:2610.16047), traces and numbers all 24 rib centerlines (R01 to R12, L01 to L12) in thoracic CT with a hybrid Semantic Context Forest plus 3D U-Net detector and bilateral Viterbi matching. On the SCR 2 mm benchmark it reports LCD 2.05 mm and 97.2% labeling accuracy on 12 evaluation scans, with honest caveats on Stage-3 tuning and a 5-fold CV at 3.64 mm / 91.8%. Code is MIT on GitHub and PyPI; weights are on Hugging Face.

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Three CARE 2026 contrast-enhanced liver MRI validation cases with red liver overlays from Swin UNETR and V-Net showing spleen spill and disconnected blobs, beside clean CIRSeg masks and the original images (Xu, Gao, Luo, Chen, arXiv:2610.09784, Fig. 3)

CIRSeg: Liver MRI Masks That Survive Scanner Shift

CIRSeg (Xu, Gao, Luo, Chen) is a cascaded nnU-Netv2 liver segmenter for CARE 2026 contrast-enhanced MRI. It trains with 3D CutMix and Nyul or histogram intensity transfer, refines a coarse prior at full resolution, can adapt at test time without source data, and drops disconnected false positives with LCC filtering. Official CARE eval: Dice 0.9713, HD95 18.31 mm; OOD test 0.9797 / 11.36. Claimed GitHub URL returned HTTP 404 on 13 October 2026. arXiv:2610.09784.

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