scholarly journals Evaluating estimation techniques in medical imaging without a gold standard: experimental validation

Author(s):  
John W. Hoppin ◽  
Matthew A. Kupinski ◽  
Donald W. Wilson ◽  
Todd E. Peterson ◽  
Benjamin Gershman ◽  
...  
2002 ◽  
Vol 9 (3) ◽  
pp. 290-297 ◽  
Author(s):  
Matthew A. Kupinski ◽  
John W. Hoppin ◽  
Eric Clarkson ◽  
Harrison H. Barrett ◽  
George A. Kastis

2020 ◽  
Vol 24 (05) ◽  
pp. 510-522
Author(s):  
Jannick De Tobel ◽  
Christian Ottow ◽  
Thomas Widek ◽  
Isabella Klasinc ◽  
Håkan Mörnstad ◽  
...  

AbstractMedical imaging for forensic age estimation in living adolescents and young adults continues to be controversial and a subject of discussion. Because age estimation based on medical imaging is well studied, it is the current gold standard. However, large disparities exist between the centers conducting age estimation, both between and within countries. This review provides an overview of the most common approaches applied in Europe, with case examples illustrating the differences in imaging modalities, in staging of development, and in statistical processing of the age data. Additionally, the review looks toward the future because several European research groups have intensified studies on age estimation, exploring four strategies for optimization: (1) increasing sample sizes of the reference populations, (2) combining single-site information into multifactorial information, (3) avoiding ionizing radiation, and (4) conducting a fully automated analysis.


Author(s):  
Menno van den Hout ◽  
Ruby S. B. Ospina ◽  
Sjoerd van der Heide ◽  
Juan Carlos Alvarado-Zacarias ◽  
Jose Enrique Antonio-Lopez ◽  
...  

2016 ◽  
Vol 74 ◽  
pp. 158-166 ◽  
Author(s):  
Maarten van Smeden ◽  
Daniel L. Oberski ◽  
Johannes B. Reitsma ◽  
Jeroen K. Vermunt ◽  
Karel G.M. Moons ◽  
...  

Author(s):  
John Chiverton ◽  
Kevin Wells

This chapter applies a Bayesian formulation of the Partial Volume (PV) effect, based on the Benford distribution, to the statistical classification of nuclear medicine imaging data: specifically Positron Emission Tomography (PET) acquired as part of a PET-CT phantom imaging procedure. The Benford distribution is a discrete probability distribution of great interest for medical imaging, because it describes the probabilities of occurrence of single digits in many sources of data. The chapter thus describes the PET-CT imaging and post-processing process to derive a gold standard. Moreover, this chapter uses it as a ground truth for the assessment of a Benford classifier formulation. The use of this gold standard shows that the classification of both the simulated and real phantom imaging data is well described by the Benford distribution.


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