  The objective of this study was to develop a PET tumor-segmentation framework
that addresses the challenges of limited spatial resolution, high image noise,
and lack of clinical training data with ground-truth tumor boundaries in PET
imaging. We propose a three-module PET-segmentation framework in the context of
segmenting primary tumors in 3D FDG-PET images of patients with lung cancer on
a per-slice basis. The first module generates PET images containing highly
realistic tumors with known ground-truth using a new stochastic and
physics-based approach, addressing lack of training data. The second module
trains a modified U-net using these images, helping it learn the
tumor-segmentation task. The third module fine-tunes this network using a
small-sized clinical dataset with radiologist-defined delineations as surrogate
ground-truth, helping the framework learn features potentially missed in
simulated tumors. The framework's accuracy, generalizability to different
scanners, sensitivity to partial volume effects (PVEs) and efficacy in reducing
the number of training images were quantitatively evaluated using Dice
similarity coefficient (DSC) and several other metrics. The framework yielded
reliable performance in both simulated (DSC: 0.87 (95% CI: 0.86, 0.88)) and
patient images (DSC: 0.73 (95% CI: 0.71, 0.76)), outperformed several widely
used semi-automated approaches, accurately segmented relatively small tumors
(smallest segmented cross-section was 1.83 cm2), generalized across five PET
scanners (DSC: 0.74), was relatively unaffected by PVEs, and required low
training data (training with data from even 30 patients yielded DSC of 0.70).
In conclusion, the proposed framework demonstrated the ability for reliable
automated tumor delineation in FDG-PET images of patients with lung cancer.
