AutoPET Challenge: Tumour Synthesis for Data Augmentation

Lap Yan Lennon Chan, Chenxin Li, Yixuan Yuan
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Abstract

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.
AutoPET 挑战:用于数据增强的肿瘤合成
全身 PET/CT 扫描中准确的病灶分割对癌症诊断和治疗计划至关重要,但有限的数据集往往会阻碍自动分割模型的性能。在本文中,我们探索了利用生成模型的深度先验作为 PET/CT 扫描中病灶自动分割的数据增强器的潜力。我们调整了最初为 CT 图像设计的 DiffTumor 方法,以生成带有病灶的合成 PET-CT 图像。我们的方法在自动 PET 数据集上训练生成模型,并用它来扩展训练数据。然后,我们比较了在原始数据集和增强数据集上训练的分割模型的性能。我们的研究结果表明,在扩增数据集上训练的模型获得了更高的 Dice 分数,证明了我们的数据扩增方法的潜力。总之,这项工作为改进数据集有限的全身 PET/CT 扫描中的病灶分割提供了一个很有前景的方向,有可能提高癌症诊断的准确性和可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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