Can Atlas-Based Auto-Segmentation Ever Be Perfect? Insights From Extreme Value Theory
| Autor: | Devis Peressutti, Tim Lustberg, Andre Dekker, Bas Schipaanboord, Johan van Soest, Wouter van Elmpt, Timor Kadir, Djamal Boukerroui, Mark Gooding |
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| Přispěvatelé: | RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, Promovendi ODB, Radiotherapie, Radiation Oncology |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
SELECTION
extreme value theory Computer science IMAGES Image processing Computed tomography 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine FUSION Atlas (anatomy) Neoplasms Image Processing Computer-Assisted medicine Humans Segmentation Electrical and Electronic Engineering Extreme value theory atlas-based segmentation Radiological and Ultrasound Technology medicine.diagnostic_test Radiotherapy Atlas (topology) Auto segmentation Radiotherapy Planning Computer-Assisted Image segmentation Computer Science Applications medicine.anatomical_structure Tomography X-Ray Computed Head Organ Sparing Treatments Algorithm auto-contouring Algorithms Neck Software |
| Zdroj: | Ieee Transactions on Medical Imaging, 38(1), 99-106. IEEE IEEE Transactions on Medical Imaging, 38(1), 99-106. Institute of Electrical and Electronics Engineers Inc. |
| ISSN: | 0278-0062 |
| Popis: | 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 ( ${N}=\textsf {316}$ ) and thoracic ( ${N}=\textsf {280}$ ) 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. |
| Databáze: | OpenAIRE |
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