Semantic segmentation of computed tomography for radiotherapy with deep learning: compensating insufficient annotation quality using contour augmentation-Reference-Cited by-同舟云学术

Semantic segmentation of computed tomography for radiotherapy with deep learning: compensating insufficient annotation quality using contour augmentation

Published:2019-03-15 Issue: Volume: Page:
ISSN:
Container-title:Medical Imaging 2019: Image Processing
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Author:

Javaid Umair,Dasnoy Damien,Lee John A.

Publisher

SPIE

Reference14 articles.

1. A survey on deep learning in medical image analysis

2. Human-level cmr image analysis with deep fully convolutional networks;Bai;arXiv preprint arXiv:1710.09289,2017

3. Evaluate the malignancy of pulmonary nodules using the 3d deep leaky noisy-or network;Liao;arXiv preprint arXiv:1711.08324,2017

4. Multi-organ segmentation of chest ct images in radiation oncology: Comparison of standard and dilated unet;Javaid,2018

5. A probabilistic u-net for segmentation of ambiguous images;Kohl;arXiv preprint arXiv:1806.05034,2018

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