A future of automated image contouring with machine learning in radiation therapy-Reference-Cited by-同舟云学术

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A future of automated image contouring with machine learning in radiation therapy

Published:2019-12 Issue:4 Volume:66 Page:223-225
ISSN:2051-3895
Container-title:Journal of Medical Radiation Sciences
language:en
Short-container-title:J Med Radiat Sci

Author:

Jackson Price¹²^ORCID,Kron Tomas¹²,Hardcastle Nicholas²³^ORCID

Affiliation:

1. Department of Oncology Sir Peter MacCallum University of Melbourne Melbourne Victoria Australia

2. Department of Physical Sciences Peter MacCallum Cancer Centre Melbourne Victoria Australia

3. Centre for Medical Radiation Physics University of Wollongong Wollongong NSW Australia

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmrs.365

Reference5 articles.

1. Implementing user‐defined atlas‐based auto‐segmentation for a large multi‐centre organisation: the Australian Experience

2. ÇiçekÖ AbdulkadirA LienkampSS BroxT RonnebergerO.3D U‐Net: learning dense volumetric segmentation from sparse annotation. Paper presented at: International conference on medical image computing and computer‐assisted intervention 2016.

3. AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy

4. SukhbaatarS BrunaJ PaluriM BourdevL FergusR.Training convolutional networks with noisy labels.arXiv preprint arXiv:14062080. 2014.

5. Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma

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3. A survey of medical dosimetrists’ perceptions of efficiency and consistency of auto-contouring software;Medical Dosimetry;2022

4. Study Design: Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning;2021-12-08

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