Segmentation of head and neck radiotherapy scans with machine learning

Full title Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on medical images including computed tomography (CT) and magnetic resonance imaging (MRI) scans captured for radiotherapy planning.
Research type Research study
IRAS ID 202595
Contact Name Carlton Chu
Contact Email carltonchu@google.com
Sponsor organisation DeepMind Technologies Ltd
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Clinicaltrials.gov identifier
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Research summary Radiotherapy is one of the main ways head and neck cancers are treated; radiation is used to kill cancerous cells and prevent their recurrence. Radiotherapy is used to treat most cases of head and neck cancer.nComplex treatment planning is required to ensure that enough radiation is given to the tumour, and little to other sensitive structures (known as organs at risk) such as the eyes and nerves which might otherwise be damaged. This is especially difficult innthe Head & Neck, where multiple at-risk structures often lie in extremely close proximity to the tumour. It can take radiotherapy experts four hours or more to pick out the important areas on planning scans (known as segmentation).nCutting edge machine learning algorithms, along with increasing availability of large digital datasets, allow the creation of sophisticated image recognition tools. Through analysis of the images used in planning radiotherapy, we aim to design a computer-based tool to automatically segment regions and improve efficiency in radiotherapy units.
REC Name South Central - Oxford C Research Ethics Committee
REC Reference 16/SC/0189
REC Opinion

Favourable Opinion

Favourable Opinion

Date of REC Opinion 6 April 2016