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Dawn Carnell
Researcher at University College London Hospitals NHS Foundation Trust
Publications - 2
Citations - 330
Dawn Carnell is an academic researcher from University College London Hospitals NHS Foundation Trust. The author has contributed to research in topics: Segmentation & Deep learning. The author has an hindex of 2, co-authored 2 publications receiving 165 citations.
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Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
Stanislav Nikolov,Sam Blackwell,R. Mendes,Jeffrey De Fauw,Clemens Meyer,Cian Hughes,Harry Askham,Bernardino Romera-Paredes,Alan Karthikesalingam,Carlton Chu,Dawn Carnell,Cheng Boon,Derek D'Souza,S. Moinuddin,Kevin Sullivan,Hugh Montgomery,Geraint Rees,Ricky A. Sharma,Mustafa Suleyman,Trevor Back,Joseph R. Ledsam,Olaf Ronneberger +21 more
TL;DR: A 3D U-Net architecture that achieves performance similar to experts in delineating a wide range of head and neck OARs is demonstrated that could improve the effectiveness of radiotherapy pathways.
Journal ArticleDOI
Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study
Stanislav Nikolov,Sam Blackwell,Alexei Zverovitch,R. Mendes,Michelle Livne,Jeffrey De Fauw,Yojan Patel,Clemens Meyer,Harry Askham,Bernadino Romera-Paredes,Christopher Kelly,Alan Karthikesalingam,Carlton Chu,Dawn Carnell,Cheng Boon,Derek D'Souza,S. Moinuddin,Bethany Garie,Yasmin McQuinlan,Sarah Ireland,Kiarna Hampton,Krystle Fuller,Hugh Montgomery,Geraint Rees,Mustafa Suleyman,Trevor Back,Cían Owen Hughes,Joseph R. Ledsam,Olaf Ronneberger +28 more
TL;DR: In this article, a 3D U-Net architecture was used to segment head and neck organs at risk commonly segmented in clinical practice, and the model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practices and segmentations created by experienced radiographers.