Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study
Journal of Medical Internet Research2021Vol. 23(7), pp. e26151–e26151
Citations Over TimeTop 1% of 2021 papers
Stanislav Nikolov, Sam Blackwell, Alexei Zverovitch, R. Mendes, Michelle Livne, Jeffrey De Fauw, Yojan Patel, Clemens Meyer, Harry Askham, Bernardino Romera‐Paredes, Christopher Kelly, Alan Karthikesalingam, Carlton Chu, Dawn Carnell, C.S. Boon, D. D’Souza, Syed Moinuddin, Bethany Garie, Yasmin McQuinlan, Sarah Ireland, Kiarna Hampton, Krystle Fuller, Hugh Montgomery, Geraint Rees, Mustafa Suleyman, Trevor Back, Cían Hughes, Joseph R. Ledsam, Olaf Ronneberger
Abstract
Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.
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