Tumor volume and changes in tumor characteristics over time are important biomarkers for cancer therapy. In this context, FDG-PET/CT scans are routinely used for staging and re-staging of cancer, as the radiolabeled fluorodeoxyglucose is taken up in regions of high metabolism. Unfortunately, these regions with high metabolism are not specific to tumors and can also represent physiological uptake by normal functioning organs, inflammation, or infection, making detailed and reliable tumor segmentation in these scans a demanding task. This gap in research is addressed by the AutoPET challenge, which provides a public data set with FDG-PET/CT scans from 900 patients to encourage further improvement in this field. Our contribution to this challenge is an ensemble of two state-of-the-art segmentation models, the nn-Unet and the Swin UNETR, augmented by a maximum intensity projection classifier that acts like a gating mechanism. If it predicts the existence of lesions, both segmentations are combined by a late fusion approach. Our solution achieves a Dice score of 72.12\% on patients diagnosed with lung cancer, melanoma, and lymphoma in our cross-validation. Code: https://github.com/heiligerl/autopet_submission

翻译：肿瘤体积和肿瘤特征随时间而变化是癌症治疗的重要生物标志。 在这方面,FDG-PET/CT扫描经常用于癌症的中转和再施压,因为在高新陈代谢地区使用放射性标签含氟脱氧甘蔗糖。 不幸的是,这些高代谢性高的地区不是肿瘤特有的地区,它们也可以代表正常功能器官的生理吸收、炎症或感染,使这些扫描中详细和可靠的肿瘤分解成为一项艰巨的任务。AutoPET挑战解决了研究中的这一差距,为900名病人的FDG-PET/CT扫描提供了一套公共数据,鼓励该领域的进一步改善。我们对此挑战的贡献是两种最新分解模型,即Nn-Unet和Swin UENTR的结合,并辅之以一个像定时机制那样的最大强度预测分解器。如果它预测了损害的存在,那么这两种分解都是通过迟聚方法结合的。 我们的解决方案实现了72.12/CT的诊断性癌症的Dice-phemigration。