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.

ABSTRACT: 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 segm

SUBMITTER: Constantino CS 

PROVIDER: S-EPMC10407010 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature