arXiv:2608. 07368v1 Announce Type: cross Abstract: Computed tomography (CT) remains the reference for 3D osseous morphometry in femoroacetabular impingement (FAI) but requires ionizing radiation and manual measurement.
By Jack Consolini, Eric A. Bogner, Meghan Sahr, Matthew F. Koff, Kevin M. Koch, Hollis G. Potter
THA-Flow is a conditional flow-matching model that generates 3‑D prosthesis geometry directly from preoperative CT scans for total hip arthroplasty. It uses separate AutoencoderKL models to compress bone anatomy and prosthesis shapes, and a 3‑D UNet to learn a flow from Gaussian noise to the prosthesis latent space conditioned on bone geometry. In a retrospective cohort of 1,355 hips, the model produced accurate acetabular and femoral geometries for 93.4% of cases, preserving component position and alignment while allowing limited local variation.
By Yiping Wang, Jie Li, Jingyu Shen, Liao Wang
arXiv:2609.05579v1 Announce Type: cross
Abstract: Predicting postoperative spinal morphology from preoperative radiographs could provide valuable support for scoliosis surgical planning, but remains...
By Rui Tang, Sicheng Yang, Moxin Zhao, Hongqiu Wang, Guankun Wang, Lei Zhu, Hongliang Ren, Menglin Cong, Nan Meng
RAUL is a reference‑assisted reconstruction framework that recovers ureteroscope trajectories from endoscopic video alone, using a high‑quality reference exploration video for each phantom. It achieves a mean translation error of 0.5 mm and increases frame‑wise localization coverage from 50.5 % to 86.1 % compared to standard Structure‑from‑Motion. The reconstructed trajectories reveal significant differences in navigation metrics between high‑ and low‑experience trainees, enabling objective skill assessment without external tracking equipment.
By Fangjie Li, Mai Bui, Charan Mohan, Michael Miga, Matthieu Chabanas, Nicholas Kavoussi, Jie Ying Wu
The paper introduces DAMM‑Net++, a 2.5D neural network for thoracic organ‑at‑risk and target volume segmentation that tackles inter‑slice surface incoherence, small low‑contrast target failure, and lack of per‑case reliability signals. Its core is an anatomy‑change‑aware bidirectional selective state‑space memory that propagates context across axial slices, complemented by a boundary‑aware decoder and an uncertainty head for calibrated per‑voxel confidence. Evaluations on 2,146 patients, an external cohort, and a reader study show high Dice scores (0.955), low HD95 (3.78 mm), significant time savings (75‑80 %) for clinicians, and improved junior‑reader performance, with the system fully integrated into a clinical workflow.
By Galib Ahmed, Istiak Ahmed, Aritra Islam Saswato, Asib Mostakim Fony, Kazi Shahriar Sanjid, Md. Tanzim Hossain, Md. Anwarul Islam, Md. Nishan Khan, Md. Misbah Khan, Labiba Faiza Karim, Jobaer Rahman, S M Hasibul Hoque, Rahnuma Shahrin Rista, Kamruzzaman Rumman, Md Arifur Rahman, Syed Md. Akram Hussain, Mohammad Ashrafuzzaman Khan, M. Monir Uddin
arXiv:2607. 11949v1 Announce Type: cross Abstract: We present a clinically deployed end-to-end auto-contouring system for cervical cancer radiotherapy planning, anchored by the Boundary-Aware Transformer with Region-Aware Mamba (BAT-RM), a hybrid architecture that integrates Sobel-gated boundary attention, a linear-time, multi-directional Mamba module for long-range context, and a boundary-skeleton-guided fusion gate.
By Istiak Ahmed, Kazi Shahriar Sanjid, Galib Ahmed, Md. Tanzim Hossain, Md. Anwarul Islam, Shahrukh Khan, Md. Ashrif Rahman Arian, Md. Nishan Khan, Md. Misbah Khan, S M Hasibul Hoque, Rahnuma Shahrin Rista, Md. Jobairul Islam, Sheikh Anisul Haque, Md Arifur Rahman, Syed Md. Akram Hussain, Syeda Nashra, Sayeed Shafayet Chowdhury, Md. Mostafa Kamal Sarker, M. Monir Uddin