  Numerous oncology indications have extensively quantified metabolically
active tumors using positron emission tomography (PET) and computed tomography
(CT). F-fluorodeoxyglucose-positron emission tomography (FDG-PET) is frequently
utilized in clinical practice and clinical drug research to detect and measure
metabolically active malignancies. The assessment of tumor burden using manual
or computer-assisted tumor segmentation in FDG-PET images is widespread. Deep
learning algorithms have also produced effective solutions in this area.
However, there may be a need to improve the performance of a pre-trained deep
learning network without the opportunity to modify this network. We investigate
the potential benefits of test-time augmentation for segmenting tumors from
PET-CT pairings. We applied a new framework of multilevel and multimodal tumor
segmentation techniques that can simultaneously consider PET and CT data. In
this study, we improve the network using a learnable composition of test time
augmentations. We trained U-Net and Swin U-Netr on the training database to
determine how different test time augmentation improved segmentation
performance. We also developed an algorithm that finds an optimal test time
augmentation contribution coefficient set. Using the newly trained U-Net and
Swin U-Netr results, we defined an optimal set of coefficients for test-time
augmentation and utilized them in combination with a pre-trained fixed nnU-Net.
The ultimate idea is to improve performance at the time of testing when the
model is fixed. Averaging the predictions with varying ratios on the augmented
data can improve prediction accuracy. Our code will be available at
\url{https://github.com/sepidehamiri/pet\_seg\_unet}
