| Autor: |
Cláudia S. Constantino, Sónia Leocádio, Francisco P. M. Oliveira, Mariana Silva, Carla Oliveira, Joana C. Castanheira, Ângelo Silva, Sofia Vaz, Ricardo Teixeira, Manuel Neves, Paulo Lúcio, Cristina João, Durval C. Costa |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Journal of Digital Imaging. |
| ISSN: |
1618-727X |
| Popis: |
The objective is to assess the performance of seven semiautomatic and two fully automatic segmentation methods on [18F]FDG PET/CT lymphoma images and evaluate their influence on tumor quantification. All lymphoma lesions identified in 65 whole-body [18F]FDG PET/CT staging images were segmented by two experienced observers using manual and semiautomatic methods. Semiautomatic segmentation using absolute and relative thresholds, k-means and Bayesian clustering, and a self-adaptive configuration (SAC) of k-means and Bayesian was applied. Three state-of-the-art deep learning–based segmentations methods using a 3D U-Net architecture were also applied. One was semiautomatic and two were fully automatic, of which one is publicly available. Dice coefficient (DC) measured segmentation overlap, considering manual segmentation the ground truth. Lymphoma lesions were characterized by 31 features. Intraclass correlation coefficient (ICC) assessed features agreement between different segmentation methods. Nine hundred twenty [18F]FDG-avid lesions were identified. The SAC Bayesian method achieved the highest median intra-observer DC (0.87). Inter-observers’ DC was higher for SAC Bayesian than manual segmentation (0.94 vs 0.84, p |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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