Auto-segmentation of organs at risk for head and neck radiotherapy planning: from atlas-based to deep learning methods

Medical Physics
Tomaž VrtovecBulat Ibragimov

Abstract

Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor cells while sparing the healthy tissues. For this purpose, target volumes and OARs have to be delineated and segmented from medical images. As manual delineation is a tedious and time-consuming task subjected to intra/inter-observer variability, computerized auto-segmentation has been developed as an alternative. The field of medical imaging and RT planning has experienced an increased interest in the past decade, with new emerging trends that shifted the field of H&N OAR auto-segmentation from atlas-based to deep learning based approaches. In this review, we systematically analyzed 78 relevant publications on auto-segmentation of OARs in the H&N region from 2008 to date, and provided critical discussions and recommendations from various perspectives: image modality - both computed tomography and magnetic resonance image modalities are being exploited, but the potential of the latter should be explored more in the future; OAR - the spinal cord, brainstem and major salivary glands are...Continue Reading

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Citations

May 18, 2020·Medical Physics·Sunan CuiIssam El Naqa
Sep 24, 2020·Medical Physics·Dong Joo RheeLaurence E Court
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