AD-GS: Sharper Dental CBCT From Fewer Projections
AD-GS fits each dental CBCT scan with 3D Gaussians, steering them toward enamel and bone so 20 to 30 projections keep root and crown edges. arXiv:2610.14963, BMVC 2026.
PSMAReg: Follow-Up PET/CT Registration That Keeps Tumor Burden
A LapIRN pipeline from Helmholtz Munich and TUM aligns follow-up PSMA PET/CT to baseline while holding MTV and TLG steady. arXiv:2610.21451, MIT code and weights.
MMA-LTS: Trust Tumor Overlay Confidence When MRI Sequences Are Missing
MMA-LTS (arXiv:2610.11419) post-hoc calibrates voxel-wise tumor overlay confidence when MRI sequences are missing, cutting ECE while keeping Dice. KAIST. No public code yet.
MID: Score Segmentation Batches Without Per-Case Labels
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: 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.
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.
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.
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.
Low-Dose CT, Low-Res MRI or Low-Count PET Slices In, Restored Series Out That Uses Neighboring Slices and Their Gaps, Still Blind to Real DICOM Spacing (ContiLNN)
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.
Whole 3D MRI Series In, Prompt-Free Organ Boxes and Report-Searchable Anatomy Out From One Frozen Encoder, Still Weak on Organs Smaller Than Its 8-Voxel Patches (A4-MR)
A4-MR, a 3D MRI vision-language model from GE HealthCare trained on about 50,000 studies (491,000 series), finds 125 anatomy classes as 3D boxes without prompts, ties each box to report text, and keeps healthy and abnormal organs apart in its embedding. Zero-shot box IoU on AMOS MR is 66.3 against 65.5 for MedSAM2 given a ground-truth box, small organs stay hard, and no code or weights are public. D’souza, Bas et al., arXiv:2610.13056.