Guides, project breakdowns, and lessons learned from building medical imaging platforms — written by the PYCAD team.
MID (arXiv:2610.17354) scores a batch of predicted segmentations against a labeled reference without per-case GT pairing, via joint MedSigLIP embeddings and a Fréchet / W2^2 distance. Apache 2.0 code on GitHub.
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.
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.
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.
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.
ContiLNN, from Kunming University of Science and Technology, XJTLU, MGH/Harvard and USTC, adds bidirectional liquid neural network modules along the slice axis of a 2D restoration network, so each restored slice draws on its neighbors and on the index gap to them. Over per-slice Restore-RWKV, mean PSNR rises 1.02 dB for MRI super-resolution and 1.25 dB for low-count PET, the CT gain is small and from one patient, and gaps are counted in slice indices rather than millimeters. Code and weights are public (Apache-2.0). He, Liu, Ding, Zhou, Wang et al., arXiv:2610.12337.
We build custom medical imaging platforms — advanced DICOM viewers, AI segmentation, and the clinical systems around them.
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