Can Atlas-Based Auto-Segmentation Ever Be Perfect? Insights From Extreme Value Theory

IEEE Transactions on Medical Imaging
Bas SchipaanboordMark Gooding

Abstract

Atlas-based segmentation is used in radiotherapy planning to accelerate the delineation of organs at risk (OARs). Atlas selection has been proposed to improve the performance of segmentation, assuming that the more similar the atlas is to the patient, the better the result. It follows that the larger the database of atlases from which to select, the better the results should be. This paper seeks to estimate a clinically achievable expected performance under this assumption. Assuming a perfect atlas selection, an extreme value theory has been applied to estimate the accuracy of single-atlas and multi-atlas segmentation given a large database of atlases. For this purpose, clinical contours of most common OARs on computed tomography of the head and neck ( N=316 ) and thoracic ( N=280 ) cases were used. This paper found that while for most organs, perfect segmentation cannot be reasonably expected, auto-contouring performance of a level corresponding to clinical quality could be consistently expected given a database of 5000 atlases under the assumption of perfect atlas selection.

Citations

Jul 30, 2019·The British Journal of Radiology·Hakan NystromPetra Witt Nystrom
Nov 12, 2019·Journal of Medical Radiation Sciences·Sarah J LewisPatrick C Brennan
May 20, 2021·International Journal of Computer Assisted Radiology and Surgery·Neeraja KonuthulaRandall A Bly
Jun 22, 2021·Nihon Hoshasen Gijutsu Gakkai zasshi·Motoharu Sasaki
Aug 4, 2021·Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology·Daan S SpoorAnne P G Crijns

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