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}
翻译:在临床实践和临床药物研究中经常使用FFDG-PET来检测和测量代谢活性恶性肿瘤。在FDG-PET图像中,使用人工或计算机辅助肿瘤分解法对肿瘤负担进行了广泛评估。深层次学习算法也在这一领域产生了有效的解决方案。然而,也许需要改进一个经过事先训练的深层学习网络的性能,而没有机会修改这个网络。我们经常在临床实践和临床药物研究中利用FDG-PET-PET来检测和测量代谢性恶性恶性肿瘤。在FDG-PET图像中,使用人工或计算机辅助肿瘤分解法对肿瘤负担进行评估是十分广泛的。在培训数据库中,我们培训了U-Net和Swin U-Net,以确定如何用不同的测试时间增强性分解性能。我们还开发了一种测试性能测试结果,用于在最新时间的固定性能测试中,我们用SMADRA值来确定一个最佳的测试结果。