Evaluation of Semiautomatic and Deep Learning–Based Fully Automatic Segmentation Methods on [18F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization.
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 tw...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 4; pp. 1864 - 1877 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=169808818&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169808818 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2023 vid: 36 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169808818 163068826 169808818 169808818 10.1007/s10278-023-00823-y 169808818 ppf: 1864 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Evaluation of Semiautomatic and Deep Learning–Based Fully Automatic Segmentation Methods on [18F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization. aug: au: Constantino, Cláudia S. Leocádio, Sónia Oliveira, Francisco P. M. Silva, Mariana Oliveira, Carla Castanheira, Joana C. Silva, Ângelo Vaz, Sofia Teixeira, Ricardo Neves, Manuel Lúcio, Paulo João, Cristina Costa, Durval C. affil: Nuclear Medicine – Radiopharmacology Department, Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal sug: subj: Lymphoma Diagnosis Fludeoxyglucose F 18 Diagnostic Use Positron Emission Tomography Computed Tomography Methods Deep Learning Automation Reproducibility of Results Human Intraclass Correlation Coefficient Intrarater Reliability Interrater Reliability Image Processing, Computer Assisted Equipment Design Funding Source ab: 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 < 0.001). Semiautomatic deep learning–based median DC was promising (0.83 (Obs1), 0.79 (Obs2)). Threshold-based methods and publicly available 3D U-Net gave poorer results (0.56 ≤ DC ≤ 0.68). Maximum, mean, and peak standardized uptake values, metabolic tumor volume, and total lesion glycolysis showed excellent agreement (ICC ≥ 0.92) between manual and SAC Bayesian segmentation methods. The SAC Bayesian classifier is more reproducible and produces similar lesion features compared to manual segmentation, giving the best concordant results of all other methods. Deep learning–based segmentation can achieve overall good segmentation results but failed in few patients impacting patients' clinical evaluation. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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