Machine Learning for Auto-Segmentation in Radiotherapy Planning.
Manual segmentation of target structures and organs at risk is a crucial step in the radiotherapy workflow. It has the disadvantages that it can require several hours of clinician time per patient and is prone to inter- and intra-observer variability. Automatic segmentation (auto-segmentation), usin...
| Publicado en: | Clinical Oncology Vol. 34; no. 2; pp. 74 - 89 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
W B Saunders
Feb2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154720177&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154720177 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09366555 LWQ jtl: Clinical Oncology issn: 09366555 maglogo: N pubinfo: dt: Feb2022 vid: 34 iid: 2 pid: 1351 pub: W B Saunders place: Philadelphia, Pennsylvania artinfo: ui: 154720177 10.1016/j.clon.2021.12.003 154720177 ppf: 74 ppct: 15 formats: tig: atl: Machine Learning for Auto-Segmentation in Radiotherapy Planning. aug: au: Harrison, K. Pullen, H. Welsh, C. Oktay, O. Alvarez-Valle, J. Jena, R. affil: Cavendish Laboratory, University of Cambridge, Cambridge, UK sug: subj: Machine Learning Radiotherapy Methods Image Processing, Computer Assisted Workflow Algorithms Deep Learning Neural Networks (Computer) ab: Manual segmentation of target structures and organs at risk is a crucial step in the radiotherapy workflow. It has the disadvantages that it can require several hours of clinician time per patient and is prone to inter- and intra-observer variability. Automatic segmentation (auto-segmentation), using computer algorithms, seeks to address these issues. Advances in machine learning and computer vision have led to the development of methods for accurate and efficient auto-segmentation. This review surveys auto-segmentation techniques and applications in radiotherapy planning. It provides an overview of traditional approaches to auto-segmentation, including intensity analysis, shape modelling and atlas-based methods. The focus, though, is on uses of machine learning and deep learning, including convolutional neural networks. Finally, the future of machine-learning-driven auto-segmentation in clinical settings is considered, and the barriers that must be overcome for it to be widely accepted into routine practice are highlighted. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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