Zobrazeno 1 - 10 of 10 pro vyhledávání: '"Jonathan Giambattista"'
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Akademický článek
Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers
Autor: Jordan Wong, Vicky Huang, Derek Wells, Joshua Giambattista, Jonathan Giambattista, Carter Kolbeck, Karl Otto, Elantholi P. Saibishkumar, Abraham Alexander
Publikováno v: Radiation Oncology, Vol 16, Iss 1, Pp 1-10 (2021)
Abstract Purpose We recently described the validation of deep learning-based auto-segmented contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study, we evaluate the performance of implemented DC models in the cli
Externí odkaz: https://doaj.org/article/afc15d7e13a2488393fe45995124b7be
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2
Akademický článek
Training and Validation of Deep Learning-Based Auto-Segmentation Models for Lung Stereotactic Ablative Radiotherapy Using Retrospective Radiotherapy Planning Contours
Autor: Jordan Wong, Vicky Huang, Joshua A. Giambattista, Tony Teke, Carter Kolbeck, Jonathan Giambattista, Siavash Atrchian
Publikováno v: Frontiers in Oncology, Vol 11 (2021)
PurposeDeep learning-based auto-segmented contour (DC) models require high quality data for their development, and previous studies have typically used prospectively produced contours, which can be resource intensive and time consuming to obtain. The
Externí odkaz: https://doaj.org/article/bbd92e72262e47e38a5834586fbc9139
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3
Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers
Autor: Jonathan Giambattista, Derek Wells, Carter Kolbeck, Karl Otto, Vicky Huang, Elantholi P. Saibishkumar, Jordan Wong, Abraham Alexander, Joshua Giambattista
Publikováno v: Radiation Oncology (London, England)
Radiation Oncology, Vol 16, Iss 1, Pp 1-10 (2021)
Purpose We recently described the validation of deep learning-based auto-segmented contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study, we evaluate the performance of implemented DC models in the clinical rad
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a904b34f96b41139d6e46c53aeba00e7
http://europepmc.org/articles/PMC8186196
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9
Comparing Deep Learning-based Auto-segmentation of Organs at Risk and Clinical Target Volumes to Expert Inter-Observer Variability in Radiotherapy Planning
Autor: Nevin McVicar, Jonathan Giambattista, Derek Wells, Jordan Wong, Sally L. Smith, Carter Kolbeck, Lovedeep Gondara, Allan Fong, Joshua Giambattista, Abraham Alexander
Publikováno v: International Journal of Radiation Oncology*Biology*Physics. 105:S70-S71
Background Deep learning-based auto-segmented contours (DC) aim to alleviate labour intensive contouring of organs at risk (OAR) and clinical target volumes (CTV). Most previous DC validation studies have a limited number of expert observers for comp
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7574c5b83bf680bce1abf43292ef6866
https://doi.org/10.1016/j.ijrobp.2019.06.523
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