Title: Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy,
Abstract: Over half a million individuals are diagnosed with head and neck cancer each
year worldwide. Radiotherapy is an important curative treatment for this
disease, but it requires manually intensive delineation of radiosensitive
organs at risk (OARs). This planning process can delay treatment commencement.
While auto-segmentation algorithms offer a potentially time-saving solution,
the challenges in defining, quantifying and achieving expert performance
remain. Adopting a deep learning approach, we demonstrate a 3D U-Net
architecture that achieves performance similar to experts in delineating a wide
range of head and neck OARs. The model was trained on a dataset of 663
deidentified computed tomography (CT) scans acquired in routine clinical
practice and segmented according to consensus OAR definitions. We demonstrate
its generalisability through application to an independent test set of 24 CT
scans available from The Cancer Imaging Archive collected at multiple
international sites previously unseen to the model, each segmented by two
independent experts and consisting of 21 OARs commonly segmented in clinical
practice. With appropriate validation studies and regulatory approvals, this
system could improve the effectiveness of radiotherapy pathways.