AutoPET挑战赛：用于数据增强的肿瘤合成
AutoPET Challenge: Tumour Synthesis for Data Augmentation
AI总结：
针对全身PET/CT扫描病灶分割数据集有限的问题，研究调整DiffTumor方法生成合成PET-CT图像用于数据增强，在AutoPET数据集上验证可提升分割模型Dice系数，为有限数据下改进病灶分割提供新方向。
摘要：
全身PET/CT扫描中的精准病灶分割对癌症诊断和治疗规划至关重要，但有限的数据集往往会制约自动分割模型的性能。本文探究了利用生成模型的深度先验作为PET/CT扫描自动病灶分割数据增强器的潜力。我们调整了原本为CT图像设计的DiffTumor方法，以生成带病灶的合成PET-CT图像。该方法在AutoPET数据集上训练生成模型，并用其扩充训练数据。随后我们对比了在原始数据集和增强数据集上训练的分割模型的性能。结果显示，在增强数据集上训练的模型取得了更高的Dice系数，证明了该数据增强方法的潜力。简言之，这项工作为在数据集有限的情况下改进全身PET/CT扫描病灶分割提供了一个有前景的方向，有望提升癌症诊断的准确性和可靠性。
英文摘要：
Accurate lesion segmentation in whole-body PET/CT scans is crucial for cancer diagnosis and treatment planning, but limited datasets often hinder the performance of automated segmentation models. In this paper, we explore the potential of leveraging the deep prior from a generative model to serve as a data augmenter for automated lesion segmentation in PET/CT scans. We adapt the DiffTumor method, originally designed for CT images, to generate synthetic PET-CT images with lesions. Our approach trains the generative model on the AutoPET dataset and uses it to expand the training data. We then compare the performance of segmentation models trained on the original and augmented datasets. Our findings show that the model trained on the augmented dataset achieves a higher Dice score, demonstrating the potential of our data augmentation approach. In a nutshell, this work presents a promising direction for improving lesion segmentation in whole-body PET/CT scans with limited datasets, potentially enhancing the accuracy and reliability of cancer diagnostics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
