arXiv:2608. 20154v1 Announce Type: new Abstract: Minimally invasive colorectal surgeries (MIS-CRS) are characterised by significant variability and inconsistent outcomes.
By Pietro Mascagni, Julia Alekseenko, Pooja P Jain, Marta Goglia, Andrea Balla, Ludovica Baldari, Gianfranco Silecchia, Claudio Fiorillo, Vincenzo Tondolo, Salvador Morales-Conde, Luigi Boni, Sergio Alfieri, Nicolas Padoy
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
The study presents a fully automated 4D U‑Net that segments the ascending aorta, arch, and proximal descending aorta in 4D flow MRI using a hybrid 4D convolutional kernel and sparse 4D labels derived from 2D expert contours and centerlines. Trained on 268 scans from eight centers, the model achieved high Dice scores (0.927 internal, 0.911 external) and excellent agreement with expert measurements of peak velocity, net flow, wall shear stress, and diameters (ICC ≥0.954 internal, ≥0.980 external). Compared to frame‑wise 3D networks and semi‑automatic methods, the 4D U‑Net outperformed in diastole and generalised well to independent post‑contrast data.
By Hinrich Rahlfs, Julio Garcia, Chiara Manini, Markus H\"ullebrand, Sebastian Schmitter, Sarah Nordmeyer, Titus K\"uhne, Heiko Stern, Christian Meierhofer, Andreas Harloff, Sebastian Kelle, Alexander Lenz, Peter Bannas, Jeanette Schulz-Menger, Ralf F Trauzeddel, Anja Hennemuth
The paper presents a Physics‑Conditioned Neural Operator (PCNO), a transformer‑based model that predicts mitral and tricuspid valve leaflet displacement, strain, and stress fields conditioned on systolic blood pressure and tissue properties. Trained on FEBio finite‑element simulations, PCNO achieves a displacement error of 4.48 % and maintains mean errors of unsupervised geometric measures within 3.5 % even when pressure and material parameters lie outside the training set, demonstrating a conditioned solution operator rather than simple interpolation. Compared to graph neural network baselines, PCNO shows superior accuracy, particularly in stress predictions.
By Shawn Koohy, Wensi Wu, Matthew A Jolley, Paris Perdikaris
arXiv:2609.23826v1 Announce Type: new
Abstract: Mitral regurgitation is the most common heart valve disorder worldwide, affecting over 2% of the global population, rising to at least 10% in adults ov...
By Shawn Koohy, Wensi Wu, Matthew A Jolley, Paris Perdikaris
The study evaluates three automated methods for distinguishing intimal from medial intracranial arterial calcifications (IACs) on non‑contrast head CT scans. Using segmentation masks, the methods—an adapted visual score, a sphericity metric, and shape embeddings from a medical foundation model—achieved comparable performance, with the embedding approach achieving the highest weighted F1 scores (71.5% for single arteries, 59.8% for joint classification). The approach remains robust when using automated versus manual segmentation masks, demonstrating feasibility for fully automated IAC subtype quantification.
By Benjamin Jin, Maria del C. Vald\'es Hern\'andez, Richard Bortsov, Joanna M. Wardlaw, Daniel Bos, Grant Mair