Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting.
Eur J Nucl Med Mol Imaging
; 48(11): 3444-3456, 2021 10.
Article
em En
| MEDLINE
| ID: mdl-33772335
ABSTRACT
PURPOSE:
In this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without relying on other image modalities or additional prior constraints, in the context of multicenter images with heterogeneous characteristics.METHODS:
In cervical cancer, an additional challenge is the location of the tumor uptake near or even stuck to the bladder. PET datasets of 232 patients from five institutions were exploited. To avoid unreliable manual delineations, the ground truth was generated with a semi-automatedapproach:
a volume containing the tumor and excluding the bladder was first manually determined, then a well-validated, semi-automated approach relying on the Fuzzy locally Adaptive Bayesian (FLAB) algorithm was applied to generate the ground truth. Our model built on the U-Net architecture incorporates residual blocks with concurrent spatial squeeze and excitation modules, as well as learnable non-linear downsampling and upsampling blocks. Experiments relied on cross-validation (four institutions for training and validation, and the fifth for testing).RESULTS:
The model achieved good Dice similarity coefficient (DSC) with little variability across institutions (0.80 ± 0.03), with higher recall (0.90 ± 0.05) than precision (0.75 ± 0.05) and improved results over the standard U-Net (DSC 0.77 ± 0.05, recall 0.87 ± 0.02, precision 0.74 ± 0.08). Both vastly outperformed a fixed threshold at 40% of SUVmax (DSC 0.33 ± 0.15, recall 0.52 ± 0.17, precision 0.30 ± 0.16). In all cases, the model could determine the tumor uptake without including the bladder. Neither shape priors nor anatomical information was required to achieve efficient training.CONCLUSION:
The proposed method could facilitate the deployment of a fully automated radiomics pipeline in such a challenging multicenter context.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Redes Neurais de Computação
Tipo de estudo:
Clinical_trials
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article