Literature DB >> 23591482

The validation index: a new metric for validation of segmentation algorithms using two or more expert outlines with application to radiotherapy planning.

Prabhjot Juneja1, Philp M Evans, Emma J Harris.   

Abstract

Validation is required to ensure automated segmentation algorithms are suitable for radiotherapy target definition. In the absence of true segmentation, algorithmic segmentation is validated against expert outlining of the region of interest. Multiple experts are used to overcome inter-expert variability. Several approaches have been studied in the literature, but the most appropriate approach to combine the information from multiple expert outlines, to give a single metric for validation, is unclear. None consider a metric that can be tailored to case-specific requirements in radiotherapy planning. Validation index (VI), a new validation metric which uses experts' level of agreement was developed. A control parameter was introduced for the validation of segmentations required for different radiotherapy scenarios: for targets close to organs-at-risk and for difficult to discern targets, where large variation between experts is expected. VI was evaluated using two simulated idealized cases and data from two clinical studies. VI was compared with the commonly used Dice similarity coefficient (DSCpair - wise) and found to be more sensitive than the DSCpair - wise to the changes in agreement between experts. VI was shown to be adaptable to specific radiotherapy planning scenarios.

Entities:  

Mesh:

Year:  2013        PMID: 23591482     DOI: 10.1109/TMI.2013.2258031

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  4 in total

1.  Designing image segmentation studies: Statistical power, sample size and reference standard quality.

Authors:  Eli Gibson; Yipeng Hu; Henkjan J Huisman; Dean C Barratt
Journal:  Med Image Anal       Date:  2017-07-22       Impact factor: 8.545

2.  The feasibility of using citizens to segment anatomy from medical images: Accuracy and motivation.

Authors:  Judith R Meakin; Ryan M Ames; J Charles G Jeynes; Jo Welsman; Michael Gundry; Karen Knapp; Richard Everson
Journal:  PLoS One       Date:  2019-10-10       Impact factor: 3.240

3.  Exploring the impact of metabolic imaging in head and neck cancer treatment.

Authors:  Diana Raquel Dias Domingues; Michelle M Leech
Journal:  Head Neck       Date:  2022-07-01       Impact factor: 3.821

4.  Assessment of fully-automated atlas-based segmentation of novel oral mucosal surface organ-at-risk.

Authors:  Jamie A Dean; Liam C Welsh; Dualta McQuaid; Kee H Wong; Aleksandar Aleksic; Emma Dunne; Mohammad R Islam; Anushka Patel; Priyanka Patel; Imran Petkar; Iain Phillips; Jackie Sham; Kate L Newbold; Shreerang A Bhide; Kevin J Harrington; Sarah L Gulliford; Christopher M Nutting
Journal:  Radiother Oncol       Date:  2016-03-09       Impact factor: 6.280

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.