arXiv AI
A digital-twin framework for forecasting treatment-day imaging with contour uncertainty in adaptive proton radiotherapy
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
1d ago
An Uncertainty-Guided Digital Twin Framework for Online Adaptive Proton Therapy in Head and Neck Cancer: A Feasibility Study
arXiv:2609.39010v1 Announce Type: cross Abstract: Objective: Head and neck (HN) proton therapy spans six to seven weeks of anatomical change, while offline replanning takes about a week. We present a...
arXiv Computer Vision
Sep 16
Anatomy-Change-Aware Bidirectional Selective State-Space Memory for Clinically Deployed Thoracic Radiotherapy Auto-Contouring
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.
arXiv Machine Learning
Sep 14
Observation-Anchored Selective Assimilation for Longitudinal Tumor-State Proxy Forecasting in Post-Treatment Glioma
The paper introduces Observation‑Anchored Selective Assimilation (OASA) for forecasting tumor‑state proxies in post‑treatment glioma patients using serial MRI observations. OASA anchors the patient‑specific state with an intermediate observation and selectively updates it via a tiered rule and voxel‑wise soft gate, outperforming baseline methods in Dice score at certain thresholds. The approach is validated on 120 patient triplets and the code is publicly released.
arXiv Computer Vision
Sep 15
TotalSynth: Robust Whole-Body Synthetic CT from MRI and CBCT
arXiv:2609.13838v1 Announce Type: new Abstract: Purpose: To develop and evaluate TotalSynth, a reusable pretrained framework for whole-body synthetic CT (sCT) generation from MRI and cone-beam CT (CB...
arXiv AI
Sep 1
Extending TotalSegmentator: Predicting Patient and Acquisition Characteristics from CT and MR Images
arXiv:2608.29348v1 Announce Type: new Abstract: Background: Patient details and acquisition metadata are important for clinical decisions, image quality control, and automated research pipelines, but...
arXiv AI
Jul 24
Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT
arXiv:2509. 21913v2 Announce Type: replace-cross Abstract: Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy.