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Vienna, Austria

ESTRO 2025

Session

Autosegmentation
Digital Poster
Physics
Clinical feasibility of Ethos autosegmentation for adaptive whole-breast cancer treatment
jessica prunaretty, France
E25-174
Artificial Intelligence (AI) Auto-Segmentation in Clinical Target Volume of Elective Nodal Delineation in Head and Neck Cancer Radiotherapy Pathway
Ian Sann Boon, United Kingdom
E25-1358
Semi-automated landmark identification in ocular X-rays for setup verification in proton therapy at CNAO
Giulia Sellaro, Italy
E25-1431
Artificial Intelligence-based Cell Survival Colony Counting Model
Shirin A. Enger, Canada
E25-1450
Trainable Morphological Post-Processing Framework for Enhanced Thoracic Organs-at-Risk Segmentation in Radiation Therapy Planning
MiaoCi Wang, Taiwan
E25-1572
A Study on Multi-Modal Automatic Segmentation of Organs at Risk by Fusing Dual-Energy CT and Elemental Density Distribution Images
Shutong Yu, China
E25-1577
Deep Learning-Based Automatic Delineation of Nasopharyngeal Carcinoma Targets Using Prior Information from Original Plan
Guanqun Zhou, China
E25-1763
Fraction MRI segmentation using AI-based contour propagation in MRgRT for lung cancer
Moritz Rabe, Germany
E25-1855
Development of an in-house tool for head and neck cancer organ-at-risk autosegmentation
Thomas Young, United Kingdom
E25-1968
The performance of deep learning tools, trained on the same dataset, for auto-segmentation of challenging organs-at-risk in the thoracic region
Sevgi Emin, Sweden
E25-2005
Evaluating Inter-Expert Variability and Model Performance in Automated Brain Metastasis Segmentation
Nikos Paragios, France
E25-2014
Has AI Changed How We Contour? A Comparative Study of Pre- and Post-AI Contouring Practices
Ciaran Malone, Ireland
E25-340
Automatic segmentation of organs-at-risk in the brain: development and comparison of CT- and MRI-based models
Emily Mäusel, Germany
E25-2033
Clinical validation of an Artificial Intelligence (AI) based auto-segmentation tool for breast radiotherapy planning
Gabriele Palazzo, Italy
E25-2055
Objective evaluation of automatic target Segmentation for brain and head & neck tumors
Mehdi Astaraki, Sweden
E25-2145
Evaluation of an AI-based autosegmentation software for head and neck vessels contouring
Nikos Paragios, France
E25-2426
Toward automatic delineation of Clinical Target Volume in glioblastoma
Iuliana Toma-Dasu, Sweden
E25-2442
Auto-segmentation for reirradiation: comparison with the de-novo setting in head and neck cancer
Chelmis Muthoni Thiong'o, United Kingdom
E25-2484
Automated Tumor Segmentation Within Atelectasis Regions in CT Images Using nnUNet: Enhancing Diagnostic Accuracy and Radiotherapy Planning
Minghua Li, The Netherlands
E25-2555
Impact of deep learning on CT-based OARs delineation for flank irradiation: a SIOP-RTSG radiotherapy panel study
Geert Janssens, The Netherlands
E25-2578
Shining a light into the Black Box: Investigating auto-contouring uncertainty
Mark Gooding, United Kingdom
E25-2640
Estimating rectal dose in prostate radiotherapy using AI segmentation on CBCT
Aodh Mac Gairbhith, Ireland
E25-2707
Assessing the long-term clinical usage of auto-segmentation for head and neck organs-at-risk
Rita Simões, The Netherlands
E25-2714
Time efficiency, geometric accuracy, and clinical impact of AI-assisted contouring in head and neck cancer radiotherapy
Johan Martin Søbstad, Norway
E25-2724
TotalSegmentator in radiation oncology: a validation study on clinical imaging data
Maksym Fritsak, Switzerland
E25-2761
Automated delineation of coronary arteries in radiotherapy planning CT using nnUnet based on data from RTOG 0617 and REQUITE trials
Danai Pletzer, Germany
E25-2839
Clinical Evaluation of 2D and 3D Deep Learning-based CT Auto-contouring in Prostate Cancer.
Maram Alqarni, United Kingdom
E25-2956
DL-based segmentation of orodental structures supports assessment of radiation dose to teeth and mandible and maxilla alveolar and basal sub-volumes
Laia Humbert-Vidan, USA
E25-2958
Comparison of training strategies for fully automated detection and segmentation of flaps volumes in computed tomography scans
Juliette Thariat, France
E25-2973
Comparing DL-based systems for automated CTV contouring in Breast Cancer Radiotherapy
Gabriele Palazzo, Italy
E25-3013
smart segmentation in head and neck radiotherapy using a SAM-Based model: SmartSAM
Yuqing Xia, USA
E25-3116
Vertebrae Growth Tracking in Paediatric CSI Patients: Correlating CT and MRI measurements for long term late effects assessment
Adam Yeo, Australia
E25-3205
Evaluating the Performance of a Novel Federated AI Learning Platform in Auto-detection and Segmentation of Brain Metastases
EYUB YASAR AKDEMIR, USA
E25-3214
Evaluating the Performance of a Novel Federated AI Learning Platform in Auto-detection and Segmentation of Intracranial Meningiomas
EYUB YASAR AKDEMIR, USA
E25-3219
Evaluating the consistency of deep learning-based autocontouring between different treatment planning system versions
Paul Doolan, Cyprus
E25-3237
AI-generated contouring: software updates and image quality impact in daily practice
Francesca di Franco, France
E25-3311
Development of a fully automated CTV segmentation model for resection cavities of brain metastases in a multi-center patient cohort
Quynh Mai Nguyen, Germany
E25-3490
Implementation of User-Centric Dashboard to Automatically Monitor the Dynamics of Lung Cancer Cachexia using AI-driven analysis of 3D CBCT images
Behzad Rezaeifar, The Netherlands
E25-3494
Automatic relabeling and QA analysis of OAR contours using auto-segmentation algorithms
Hannu Laaksonen, Finland
E25-3539
Deep Learning for Automated Ventricle and Periventricular Space Segmentation on CT and MRI in Neuro-Oncology patients
Wouter van Elmpt, The Netherlands
E25-3554
Dentofacial structures delineation in head and neck rhabdomyosarcoma patients
Jasima Latif, United Kingdom
E25-3584
3D DeepLab-based Automated GTV Segmentation in Head and Neck Cancer Using PET/CT Imaging
Philip Wheeler, United Kingdom
E25-3700
Assessing the ETHOS contouring efficacy --- An AI study in the wild.
Frank Van den Heuvel, Switzerland
E25-613
Zero-shot auto-segmentation of rectal cancer CTV for MRI-guided online adaptive radiotherapy prompted with pre-treatment delineations
Nicole Ferreira Silverio, The Netherlands
E25-3762
AI-based contouring in MR gynaecologic brachytherapy workflow: A Practical Evaluation
Clélie CASTEX, France
E25-3763
Robust and Uncertainty-Aware Segmentation Ensemble Analysis in Radiotherapy with Contour Depths
Nicolas F. Chaves-de-Plaza, Colombia
E25-3998
Cross-institutional validation of prostate tumor auto-segmentation using multiparametric MRI
Ruben Bosschaert, The Netherlands
E25-4013
Enhanced Efficiency in GTV Delineation: Evaluating AI 'Active Contouring' Tool
Remus-Cosmin Stoica, Romania
E25-4166
Deep learning PET/CT?based algorithm for estimating tumor burden in metastatic melanoma patients under immunotherapy
Sebastian M. Christ, Switzerland
E25-4207
An evaluation of rectum contours generated by commercial deep learning contouring software using geometry, dosimetry and predicted toxicity.
Owen Mc Laughlin, United Kingdom
E25-4209
Conditioned patient-specific auto-segmentation in MRI-guided radiotherapy for pancreatic cancer: a substitute for image registration
Bjoern Eiben, United Kingdom
E25-4266
Evaluating the organizational impact of auto-segmentation software on clinical workflow: a one-year review
Jean-Luc Ley, Canada
E25-4277
Future liver remnant meets the future of medicine: AI integration in liver metastases assessment and treatment selection
Sabina Sucuri, Romania
E25-4284
Use Stochastic Differential Equations to Disentangle Data and Knowledge Uncertainty in Gross Tumor Volume Contouring
Chuxin Zhang, Belgium
E25-753
Robustness of automatic organ segmentation on various image contrasts from photon-counting CT
Patrick Wohlfahrt, Germany
E25-4458
Evaluation of an AI Algorithm for Breast Cancer Radiotherapy Target Delineation within a Multi-institutional Network
sushil beriwal, USA
E25-852
Determination of metrics by correlation between physician satisfaction and geometric similarity for heart auto-segmentation model
Eun Jeong Heo, Korea Republic of
E25-944
Organs-at-risk autocontouring on synthetic CTs for brain- and head & neck-MR-only radiotherapy workflows
Martin Buschmann, Austria
E25-1152