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ESTRO 2023
Programme
Automation
12 May 2023 - 16 May 2023
Vienna, Austria
ESTRO 2023
Session
Automation
Session Type:
Poster (Digital)
Track:
Physics
Journey:
Add to
My Programme
Head and Neck high-risk lymph nodes detection – a three-dimensional deep learning proposal
YungFa Lu
,
Taiwan
Presentation Number:
PO-1614
Automated treatment planning – Three-year clinical summary of dosimetry and user experience
jackie wu
,
USA
Presentation Number:
PO-1615
The importance of regional analysis in contour comparison
Hannah Chamberlin
,
United Kingdom
Presentation Number:
PO-1616
Multicentric evaluation of a machine learning model to streamline the RT patient-specific QA process
Nicola Lambri
,
Italy
Presentation Number:
PO-1617
Automation of the Picket Fence quality control test through digital image processing
Rodrigo Astudillo
,
Spain
Presentation Number:
PO-1618
AI-based classifier using geometrical features to minimize heart dose in left breast with VMAT
Andrea Bresolin
,
Italy
Presentation Number:
PO-1619
Transferability of deep learning models to the segmentation of gross tumour volume in brain cancer
Emiliano Spezi
,
United Kingdom
Presentation Number:
PO-1620
Evaluation of a CT scanner based deep learning auto-contouring solution for lung radiotherapy
Matthew Williams
,
United Kingdom
Presentation Number:
PO-1622
MCO-based training of RapidPlan for lung cancer improves sparing of heart and oesophagus
Liv Bolstad Hysing
,
Norway
Presentation Number:
PO-1623
Reproducibility of AI-based contour generation on synthetic CT
Ivan Coric
,
Germany
Presentation Number:
PO-1625
Interobserver study of deep learning-based segmentation for nodal target volumes in breast cancer
Nienke Hoekstra
,
The Netherlands
Presentation Number:
PO-1627
Dosimetric Evaluation of MR-based Deep Learning Automatic Contouring in the Pelvis
JJ Wyatt
,
United Kingdom
Presentation Number:
PO-1628
Lexicographic optimization-based planning for brain metastasis radiosurgery with coplanar arcs
Sara Trivellato
,
Italy
Presentation Number:
PO-1630
Commercial deep learning-based automated treatment planning validation for oropharyngeal cancer
Frank Dankers
,
The Netherlands
Presentation Number:
PO-1631
deep learning-based automatic segmentation of rectal tumors in endoscopy images
Alana Thibodeau-Antonacci
,
Canada
Presentation Number:
PO-1632
Clinical evaluation of deep learning-based nodal structures segmentation for gynecological cancers
Shrikant Deshpande
,
Australia
Presentation Number:
PO-1633
Validation of a U-Net-based algorithm for MRI-guided extremity soft tissue sarcoma GTV segmentation
Lucas Zander
,
Germany
Presentation Number:
PO-1634
Evaluation of CBCT-based auto contouring for online adaptive radiotherapy of cervical cancer
Catherine Khamphan
,
France
Presentation Number:
PO-1635
Comprehensive analysis of different commercial auto-segmentation tools for multi-site OAR contouring
Gerd Heilemann
,
Austria
Presentation Number:
PO-1636
Deep learning-based IMRT treatment planning on synthetic-CT for ART in NSCLC-patients
Dylan Callens
,
Belgium
Presentation Number:
PO-1637
Physician-specific preferences yield minor differences in knowledge-based planning generated plans
Robert Kaderka
,
USA
Presentation Number:
PO-1638
Autosegmentation of structures for Cranial Spinal Irradiation patients using Deep Learning
Jesper Kallehauge
,
Denmark
Presentation Number:
PO-1639
MRI-based deep learning autocontouring: Evaluation and implementation for brain radiotherapy
Nouf Alzahrani
,
United Kingdom
Presentation Number:
PO-1640
Clinical evaluation of autosegmentation using AI with manual segmentation of breast tissue
Remus-Cosmin Stoica
,
Romania
Presentation Number:
PO-1641
Towards automated treatment planning for robotic stereotactic radiosurgery
Marcel Nachbar
,
Germany
Presentation Number:
PO-1643
Robustness of nodal outlining software for breast radiotherapy
Alison McBride
,
United Kingdom
Presentation Number:
PO-1644
Deep learning and atlas-based approaches for Total Marrow and Lymphoid Irradiation segmentation
Damiano Dei
,
Italy
Presentation Number:
PO-1645
KB plan optimization models’ transferability: multi-institutional international consortia validation
Lorenzo Placidi
,
Italy
Presentation Number:
PO-1646
Novel dataset validation of deep learning models for autocontouring of head and neck, and prostate
Daniel Sandys
,
United Kingdom
Presentation Number:
PO-1647
Deep Learning-Based Automatic Segmentation for Brain OARs: Accuracy and Dosimetric Impact
Andrada Turcas
,
Romania
Presentation Number:
PO-1648
Style-based generative model to reconstruct head and neck 3D CTs
Alexandre Cafaro
,
France
Presentation Number:
PO-1649
A 3D transfer learning approach for identifying multiple simultaneous errors during radiotherapy
Cecile Wolfs
,
The Netherlands
Presentation Number:
PO-1650
Inter-institute transferability of KB plan prediction models for FB LWB on DIBH candidates
Alessia Tudda
,
Italy
Presentation Number:
PO-1651
KB multi-institutional plan prediction of the left whole breast irradiation with tangential fields
Alessia Tudda
,
Italy
Presentation Number:
PO-1652
Auto-contouring of cardiac avoidance region for cardiac sparing lung radiotherapy
Tom Marchant
,
United Kingdom
Presentation Number:
PO-1653
Does data curation matter in deep learning segmentation? Clinical vs edited GTVs in glioblastoma.
Kim Hochreuter
,
Denmark
Presentation Number:
PO-1654
Multi-center auto-segmentation model for internal mammary nodes using clinical data: A DBCG study
Emma Skarsø Buhl
,
Denmark
Presentation Number:
PO-1655
autoencoder-based quality assurance of deep learning segmentation of parotid glands in HNC patients
Alessia De Biase
,
The Netherlands
Presentation Number:
PO-1656
Clinical validation of AI-based CT auto-contouring for prostate cancer Radiotherapy
Claudio Fiorino
,
Italy
Presentation Number:
PO-1657
The impact of LIMBUS AI based contouring on the efficiency of prostate radiotherapy planning
Kishen Patel
,
United Kingdom
Presentation Number:
PO-1659
Automated Clinical Treatment Planning for breast: from manual to auto planning in Clinical Practise
Anna Vella
,
United Kingdom
Presentation Number:
PO-1660
AI-based auto-segmentation: advantages in delineation, absorbed dose-distribution and logistics
Gustavo R. Sarria
,
Germany
Presentation Number:
PO-1662
When no news is good news: commercial automated EPID in vivo dosimetry deployed as a safety check
Rhydian Caines
,
United Kingdom
Presentation Number:
PO-1665
Automated clinical-criteria-driven optimal planning: clinical experience with over 6000 patients
Linda Hong
,
USA
Presentation Number:
PO-1666
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