Journal
Journal of Digital Imaging
Publication Date
5-23-2021
Volume
34
Issue
3
First Page
541
Last Page
553
Document Type
Open Access Publication
DOI
10.1007/s10278-021-00460-3
Rights and Permissions
Kiser, K.J., Barman, A., Stieb, S. et al. Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow. J Digit Imaging 34, 541–553 (2021). https://doi.org/10.1007/s10278-021-00460-3 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Recommended Citation
Kiser, Kendall J; Barman, Arko; Stieb, Sonja; Fuller, Clifton D; and Giancardo, Luca, "Novel autosegmentation spatial similarity metrics capture the time required to correct segmentations better than traditional metrics in a thoracic cavity segmentation workflow." Journal of Digital Imaging. 34, 3. 541 - 553. (2021).
https://digitalcommons.wustl.edu/oa_4/1396
