S1555
Physics - Autosegmentation
ESTRO 2026
Digital Poster 1274 SpecCTSegNet: A Physics-Informed Attention Network for Automated OAR and Tumor Segmentation in Multi-Energy PC-CBCT for Preclinical Radiotherapy Xinhong Wu 1 , Maxin Chen 1 , Julie Lascaud 1 , Daniel Berthe 2,3 , Franz Pfeiffer 2,3 , Katia Parodi 4 1 Department of Medical Physics, Ludwig-Maximilians- Universität München, Munich, Germany. 2 Department of Physics, Technical University of Munich, munich, Germany. 3 Munich Institute of Biomedical Engineering, Technical University of Munich, munich, Germany. 4 faculty of physics, Ludwig-Maximilians-Universität München, Munich, Germany Purpose/Objective: Accurate segmentation of organs-at-risk (OARs) and tumors in preclinical radiotherapy research is challenged by the low contrast-to-noise ratio of conventional cone-beam CT (CBCT). Photon-counting CBCT (PC-CBCT) provides multi-energy bin spectral data, offering improved tissue differentiation. We aimed to 1) develop and evaluate a novel deep learning architecture, SpecCTSegNet, designed to exploit this spectral information, and 2) quantify how segmentation accuracy scales with spectral binning (single-, dual-, vs. tri-bin). Material/Methods: We designed SpecCTSegNet, a U-Net-based architecture featuring three key innovations: a physics- informed input block to fuse spectral channels, residual blocks with Squeeze-and-Excitation (SE) attention for adaptive feature recalibration, and an Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale context. A dedicated pipeline was developed to simulate realistic spectral PC-CBCT from 83 digital phantoms with expert-annotated structures (13 OARs + tumors per mouse) [1]. Spectral projections modeled polychromatic X-ray attenuation (65 kVp) through tissue-specific attenuation coefficients, incorporated an analytically generated 80-channel detector response (1-80 keV), and included Poisson noise. After applying a 20 keV threshold, projections were reconstructed using filtered back-projection. Three datasets were created: single-bin (20-80 keV), dual-bin (low: 20-36 keV, high: 37-80 keV), and tri-bin (low: 20-31 keV, medium: 32-41 keV, high: 42-80 keV). The network was trained independently for each configuration (75 training, 8 test cases) and evaluated using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95). Results:
Conclusion: vDSC and sDSC offer the strongest correlations with clinician scoring and editing time, suggesting their potential as surrogates for clinical validation of AI- generated contours.However, poorer correlations were seen for bowel, highlighting challenges in using quantitative metrics for large structures that have high quantitative accuracy but contain discrepancies in localised clinically-significant areas, generating low QS and high AT. Encouragingly, the strong correlations observed for the highly variable sigmoid segmentation suggests that quantitative measures can reliably reflect clinical utility. This will enable more efficient assessment of improvements in sigmoid accuracy when refining and retraining the model. Whilst these findings support the use of quantitative metrics to streamline AI segmentation evaluation, further testing is required over further treatment regions and OARs. References: [1] Image-Based Deep Learning Enables the Reduction of Gastro-Intestinal Toxicity in Pelvic Radiotherapy, Thomas, C. (Author). 1 Oct 2023, Student thesis: PhD Thesis[2] Implementation of in-house pelvic radiotherapy auto-contouring in real world clinical practice, Luis Ribeiro et. Al. RCR Global AI Conference 2025 Proceedings, 2025[3] Automated Contouring and Planning in Radiation Therapy: What Is ‘Clinically Acceptable’? Baroudi H, et al. Diagnostics. 2023; 13(4):667. Keywords: Autosegmentation, pelvis, metrics
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