Journal article
A unified framework for simultaneous assessment of accuracy, between-, and within-reader variability of image segmentations
Statistical methods in medical research, Vol.29(11), pp.96228022092089-3152
05/20/2020
DOI: 10.1177/0962280220920894
PMID: 32432517
Abstract
Medical imaging is utilized in a wide range of clinical applications. To enable a detailed quantitative analysis, medical images must often be segmented to label (delineate) structures of interest; for example, a tumor. Frequently, manual segmentation is utilized in clinical practice (e.g., in radiation oncology) to define such structures of interest. However, it can be quite time consuming and subject to substantial between-, and within-reader variability. A more reproducible, less variable, and more time efficient segmentation approach is likely to improve medical treatment. This potential has spurred the development of segmentation algorithms which harness computational power. Segmentation algorithms’ widespread use is limited due to difficulty in quantifying their performance relative to manual segmentation, which itself is subject to variation. This paper presents a statistical model which simultaneously estimates segmentation method accuracy, and between- and within-reader variability. The model is simultaneously fit for multiple segmentation methods within a unified Bayesian framework. The Bayesian model is compared to other methods used in literature via a simulation study, and application to head and neck cancer PET/CT data. The modeling framework is flexible and can be employed in numerous comparison applications. Several alternate applications are discussed in the paper.
Details
- Title: Subtitle
- A unified framework for simultaneous assessment of accuracy, between-, and within-reader variability of image segmentations
- Creators
- Andrew E Ghattas - Bank of AmericaReinhard R Beichel - University of IowaBrian J Smith - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Statistical methods in medical research, Vol.29(11), pp.96228022092089-3152
- DOI
- 10.1177/0962280220920894
- PMID
- 32432517
- ISSN
- 0962-2802
- eISSN
- 1477-0334
- Language
- English
- Date published
- 05/20/2020
- Academic Unit
- Electrical and Computer Engineering; Biostatistics; Holden Comprehensive Cancer Center
- Record Identifier
- 9984197089902771
Metrics
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