用生成模型合成肿瘤图像，提升有限数据下的肿瘤分割精度。
AutoPET Challenge: Tumour Synthesis for Data Augmentation
- 基于生成模型的深度先验，合成带肿瘤的PET-CT图像用于数据增强。
- 在AutoPET数据集上训练后，分割模型Dice分数显著提升。
- 适合医学影像领域研究者，尤其关注小样本下分割任务的场景。
全身影像中精确识别病灶对癌症诊断和治疗计划至关重要，但数据量有限常制约自动化分割模型的表现。本文探索利用生成模型的深层先验作为数据增强手段，提升PET/CT图像中病灶自动分割的效果。我们将原为CT设计的DiffTumor方法适配至PET-CT，基于AutoPET数据集训练生成模型以合成含病灶的图像，并对比原始与增强数据集上训练的分割模型性能。结果表明，使用增强数据训练的模型取得更高Dice分数，验证了该数据增强策略的有效性。本工作为解决小样本下全身影像病灶分割提供了新方向，有望提升癌症诊断的准确性和可靠性。
原文摘要 · Abstract (English)
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.
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