arXiv:2505. 17338v3 Announce Type: replace-cross Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) require prohibitive per-scan optimization (hours for NeRF, about 30 minutes for 3DGS), making them impractical in clinical settings.
By Zhongpai Gao, Benjamin Planche, Meng Zheng, Anwesa Choudhuri, Van Nguyen Nguyen, Terrence Chen, Ziyan Wu
FactorSplat introduces a per-scene N‑dimensional Gaussian splatting proxy that allows region‑specific intensity‑to‑RGBA curves to be applied at inference, enabling appearance control in medical volume rendering. The method uses a shared functional encoder and low‑rank per‑Gaussian factors to learn residual appearance while keeping geometry and directional appearance shared across presets. Experiments on seven CT and MR scans show that FactorSplat outperforms region‑aware VEG in mean PSNR and changed‑region error across validation, interpolation, unseen composition, and out‑of‑distribution edits, achieving 1.10 to 1.52 dB PSNR gains and supporting fast TF switches at 524 FPS.
By Zhongpai Gao, Benjamin Planche, Meng Zheng, Anwesa Choudhuri, Terrence Chen, Ziyan Wu
arXiv:2609.16775v1 Announce Type: new
Abstract: Annotating large radiology datasets is bottlenecked by the manual effort of delineating structures slice-by-slice in 3D volumes. Interactive methods re...
By Abhilaksh Singh Reen, Kushal Borkar, Ritvik Mahapatra
The paper introduces LiftXR, a geometry‑guided framework that first reconstructs a 3D anatomical layout from bi‑planar X‑ray images and then uses this layout to guide CT volume reconstruction. An anatomical parser refines the layout by analyzing the reconstructed CT, enabling region‑specific intensity calibration. Experiments on two public datasets show LiftXR surpasses recent X‑ray‑to‑CT methods and improves downstream segmentation performance.
By Yifei Wu, Yicheng Wu, Qiang Ma, Qi Chen, Renyang Gu, Xinyu Liu, Yongsheng Pan, Yong Xia
arXiv:2606.18623v2 Announce Type: replace
Abstract: Gaussian segmentation is usually posed as transferring object knowledge from 2D foundation models into a 3D representation. This leaves a fundament...
By Mohamed Rayan Barhdadi, Hasan Yazar, Erchin Serpedin, Mehmet Tuncel, Hasan Kurban
DALE-CT introduces depth‑aware 2D slice encoders that learn an anatomical world model of chest CT scans without 3D or positional supervision. By sampling self‑supervised views across a physical $z$‑axis slab, the encoder captures how anatomy changes between neighboring slices, enabling it to recover slice ordering and distinguish slices by anatomy alone. The model, trained on a large 287k‑scan corpus, achieves state‑of‑the‑art performance on CT‑RATE and is released with full code and evaluation tools.
By Evan W. Damron, Mahmut S. Gokmen, Mitchell A. Klusty, Caroline N. Leach, Emily B. Collier, V. K. Cody Bumgardner