Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study.

Stanislav Nikolov Sam Blackwell Alexei Zverovitch Ruheena Mendes Michelle Livne Jeffrey De Fauw Yojan Patel Clemens Meyer Harry Askham Bernardino Romera-Paredes Christopher J Kelly Alan Karthikesalingam Carlton Chu Dawn Carnell Cheng Boon Derek D'Souza Syed Ali Moinuddin Bethany Garie Yasmin McQuinlan Sarah Ireland Kiarna Hampton Krystle Fuller Hugh E Montgomery Geraint Rees Mustafa Suleyman Trevor Back Cían Owen Hughes Joseph R Ledsam Olaf Ronneberger

Published in: Journal of medical Internet research (2021)

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.

Keyphrases

deep learning
early stage
convolutional neural network
locally advanced
artificial intelligence
radiation therapy
radiation induced
machine learning
transcription factor
rectal cancer