Abstract
In this paper, we propose PPCNet (Projection-Conditioned Point Cloud Network), a query-refinement framework that reconstructs a dense 8,192-point cloud of spinal vertebrae from two orthogonal digitally reconstructed radiographs (DRRs) and their corresponding calibrated 3 × 4 projection matrices. PPCNet employs the calibrated projection geometry as the core 2D-to-3D lifting mechanism, combining occupancy-gated query initialisation with projection-conditioned refinement. Gap-preserving and curvature losses maintain inter-vertebral disc spaces and the spinal lordotic curve. Evaluated on the lumbar spine (L1–L5) across 105 held-out patients from a 1,037-patient dataset derived from VerSe and CTSpine1K, PPCNet achieves a mean Chamfer distance of 1.981 mm, F-score@5 mm of 0.973, and HD95 of 4.525 mm. Clinical validation across 525 vertebrae confirms a morphometric MAE of 2.80 mm with 97% of vertebrae at Vertebral Compression Level (VCL) Grade A. As a proof-of-concept, phantom-based navigation tracking yields a mean vertebra centroid error of 3.01 mm across L1–L5. The implementation and dataset are available at: PPCNet.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MLMI_045.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=JDVwb5tXah
BibTex
@InProceedings{G.Roh_PPCNet_MICCAISAT2026,
author = { G. K., Rohith Reddy AND Pandurang AND Maik, Vivek AND Lakshmanan, Manojkumar AND Sivaprakasam, Mohanasankar},
title = { { PPCNet: Projection-Conditioned Point Cloud Reconstruction of Spinal Vertebrae from Biplanar Radiographs } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17272},
month = {pending},
page = {pending}
}