论文标题
通过测试时间增加，全身FDG/PET-CT中改进了自动病变细分
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time Augmentation
论文作者
论文摘要
许多肿瘤学指示使用正电子发射断层扫描（PET）和计算机断层扫描（CT）对代谢活性肿瘤进行了广泛定量。 F-氟脱氧葡萄糖 - 固醇发射断层扫描（FDG-PET）经常用于临床实践和临床药物研究中，以检测和测量代谢活跃的恶性肿瘤。在FDG-PET图像中使用手动或计算机辅助肿瘤分割对肿瘤负担的评估是广泛的。深度学习算法也在该领域产生了有效的解决方案。但是，可能需要提高预训练的深度学习网络的性能，而没有机会修改该网络。我们研究了测试时间增加对从PET-CT配对分割肿瘤的潜在益处。我们应用了一个可以同时考虑PET和CT数据的多层次和多模式肿瘤分割技术的新框架。在这项研究中，我们使用可学习的测试时间扩展组成来改善网络。我们在培训数据库上训练了U-NET和SWIN U-NETR，以确定不同的测试时间增加如何改善细分性能。我们还开发了一种算法，该算法发现最佳的测试时间扩展贡献系数集。使用新训练的U-NET和SWIN U-NETR结果，我们定义了一组最佳的测试时间增强系数，并将其与预先训练的固定NNU-NET结合使用。最终的想法是在固定模型时在测试时提高性能。在增强数据上以不同比率的预测平均可以提高预测准确性。我们的代码将在\ url {https://github.com/sepidehamiri/pet \ _seg_unet}上找到。
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}
