Real-time deep neural network-based automatic bowel gas segmentation on X-ray images for particle be...
Real-time deep neural network-based automatic bowel gas segmentation on X-ray images for particle beam treatment
About this item
Full title
Author / Creator
Publisher
Dordrecht: Springer Nature B.V
Journal title
Language
English
Formats
Publication information
Publisher
Dordrecht: Springer Nature B.V
Subjects
More information
Scope and Contents
Contents
Since particle beam distribution is vulnerable to change in bowel gas because of its low density, we developed a deep neural network (DNN) for bowel gas segmentation on X-ray images. We used 6688 image datasets from 209 cases as training data, 736 image datasets from 23 cases as validation data and 102 image datasets from 51 cases as test data (tot...
Alternative Titles
Full title
Real-time deep neural network-based automatic bowel gas segmentation on X-ray images for particle beam treatment
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2818576476
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2818576476
Other Identifiers
ISSN
0158-9938
E-ISSN
1879-5447
DOI
10.1007/s13246-023-01240-9
