A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy.

Mackay, K; Bernstein, D; Glocker, B; Kamnitsas, K; Taylor, A

Mackay, K; Bernstein, D; Glocker, B; Kamnitsas, K; Taylor, A.

Affiliation

Mackay K; The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK. Electronic address: katherine.mackay@icr.ac.uk.
Bernstein D; The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK.
Glocker B; Department of Computing, Imperial College London, South Kensington Campus, London, UK.
Kamnitsas K; Department of Computing, Imperial College London, South Kensington Campus, London, UK; Department of Engineering Science, University of Oxford, Oxford, UK.
Taylor A; The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK.

Clin Oncol (R Coll Radiol) ; 35(6): 354-369, 2023 06.

Article in En | MEDLINE | ID: mdl-36803407

Subject(s)

Radiation Oncology; Radiotherapy Planning, Computer-Assisted; Humans; Radiotherapy Planning, Computer-Assisted/methods; Benchmarking; Radiometry/methods; Organs at Risk

Key words

Artificial intelligence; Auto-contour; Auto-segmentation; Deep learning; Evaluation; Radiotherapy

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Radiotherapy Planning, Computer-Assisted / Radiation Oncology Type of study: Etiology_studies / Guideline / Prognostic_studies / Qualitative_research Limits: Humans Language: En Journal: Clin Oncol (R Coll Radiol) Journal subject: NEOPLASIAS Year: 2023 Document type: Article Country of publication: United kingdom

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