Training-free generation of 4D CT and other rare acquisitions improves segmentation across modalities

Rationale: Routine clinical imaging captures anatomy under a limited set of physiologic and acquisition conditions, leaving segmentation models with sparse examples of how lesions and surrounding tissues deform with compression, respiration, or vascular pulsation. Standard image augmentation expands training datasets but does not explicitly model the mechanics underlying these changes. Patient-specific biomechanical simulation offers an approach to generate anatomically consistent images and corresponding segmentation masks under conditions absent from the original scan, without additional imaging or training a generative model. We hypothesized that incorporating this mechanistically grounded variability into training improves segmentation across imaging modalities. Methods: We evaluate across three segmentation tasks: breast cancer on 3D digital breast tomosynthesis, lung tumors on chest CT, and carotid plaque on ultrasound. From each routine scan, patient-specific biomechanical models generate acquisitions absent from the original study—a range of breast compressions, the respiratory cycle, and arterial wall distension across the cardiac cycle. The resulting displacement fields deform both images and masks. Segmentation models were trained with and without the generated data across a range of standard image augmentation techniques, evaluated via cross-validation and held-out test sets. Realism was assessed through blinded reader studies, and generated respiratory motion was validated against real 4D CT. Results: Training on the generated acquisitions improved segmentation across modalities, increasing Dice scores by 6.6% for breast (p<0.05), 10.1% for lung (p<0.05), and 4.5% for carotid plaque (p>0.05) relative to the routine scans alone. Generated respiratory motion closely matched real gated 4D CT, with Dice of 0.94 ± 0.02 for lung volume overlap. Blinded readers discriminated generated from real images at near chance (AUC: breast 0.61, lung 0.41, carotid 0.46). Conclusions: Mechanistic simulation generates imaging acquisitions that clinical systems do not supply, without training data or additional scanning, and improves AI-based disease detection across modalities.