Edge roughness quantifies impact of physician variation on training and performance of deep learning...
Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between different observers. Artificial intelligence (AI) can assist with segmentation, but challenges exist in ensuring high-quality segmentation, especially for small, variable structures, such as the esop...
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Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus
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TN_cdi_doaj_primary_oai_doaj_org_article_0d3314f05d744208ab8a55d3f5e81d38
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_0d3314f05d744208ab8a55d3f5e81d38
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-023-50382-z
