期刊
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
卷 366, 期 1874, 页码 2361-2375出版社
ROYAL SOC
DOI: 10.1098/rsta.2008.0040
关键词
segmentation; validation; expectation-maximization; magnetic resonance imaging; MRI; tumour
资金
- NCI NIH HHS [R03 CA126466] Funding Source: Medline
- NCRR NIH HHS [P41 RR013218, P41 RR019703, U41 RR019703, R01 RR021885] Funding Source: Medline
- NIBIB NIH HHS [R01 EB008015] Funding Source: Medline
- NICHD NIH HHS [P30 HD018655] Funding Source: Medline
- NIGMS NIH HHS [R01 GM074068] Funding Source: Medline
The accuracy and precision of segmentations of medical images has been difficult to quantify in the absence of a 'ground truth' or reference standard segmentation for clinical data. Although physical or digital phantoms can help by providing a reference standard, they do not allow the reproduction of the full range of imaging and anatomical characteristics observed in clinical data. An alternative assessment approach is to compare with segmentations generated by domain experts. Segmentations may be generated by raters who are trained experts or by automated image analysis algorithms. Typically, these segmentations differ due to intra-rater and inter-rater variability. The most appropriate way to compare such segmentations has been unclear. We present here a new algorithm to enable the estimation of performance characteristics, and a true labelling, from observations of segmentations of imaging data where segmentation labels may be ordered or continuous measures. This approach may be used with, among others, surface, distance transform or level-set representations of segmentations, and can be used to assess whether or not a rater consistently overestimates or underestimates the position of a boundary.
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