用自监督预训练提升PET/CT肿瘤分割精度，融合解剖与功能信息。
Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation
- 用掩码自编码器在无标注数据上预训练，提取多模态特征。
- 联合使用PET和CT信息，分割Dice达0.582，优于无预训练的0.543。
- 适合医学影像分析、深度学习初学者及临床辅助诊断研究者。
全身PET/CT影像中病灶的精准分割对肿瘤表征、治疗规划和疗效评估至关重要，但当前人工流程耗时且存在观察者间差异。自动化深度学习方法虽有潜力，但仍受限于模态特异性、孤立时间点或缺乏专家知识整合。为此，我们提出一个两阶段病灶分割框架，用于第四届AutoPET挑战赛。第一阶段采用掩码自编码器（MAE）在无标注PET/CT及纵向CT扫描上进行自监督预训练，无需人工标注即可提取鲁棒的模态特异性表征。第二阶段将预训练编码器微调，结合双向XLSTM结构与残差网络（ResNet）块及卷积解码器。通过联合利用解剖（CT）与功能（PET）信息作为互补输入通道，实现更优的时间与空间特征融合。在AutoPET Task 1数据集上的评估表明，自监督预训练显著提升分割准确率，Dice分数达0.582，优于无预训练的0.543。结果凸显了自监督学习与多模态融合结合在构建稳健、可泛化PET/CT病灶分割模型方面的潜力。代码将在https://github.com/RespectKnowledge/AutoPet_2025_BxLSTM_UNET_Segmentation公开。
原文摘要 · Abstract (English)
The accurate segmentation of lesions in whole-body PET/CT imaging is es-sential for tumor characterization, treatment planning, and response assess-ment, yet current manual workflows are labor-intensive and prone to inter-observer variability. Automated deep learning methods have shown promise but often remain limited by modality specificity, isolated time points, or in-sufficient integration of expert knowledge. To address these challenges, we present a two-stage lesion segmentation framework developed for the fourth AutoPET Challenge. In the first stage, a Masked Autoencoder (MAE) is em-ployed for self-supervised pretraining on unlabeled PET/CT and longitudinal CT scans, enabling the extraction of robust modality-specific representations without manual annotations. In the second stage, the pretrained encoder is fine-tuned with a bidirectional XLSTM architecture augmented with ResNet blocks and a convolutional decoder. By jointly leveraging anatomical (CT) and functional (PET) information as complementary input channels, the model achieves improved temporal and spatial feature integration. Evalua-tion on the AutoPET Task 1 dataset demonstrates that self-supervised pre-training significantly enhances segmentation accuracy, achieving a Dice score of 0.582 compared to 0.543 without pretraining. These findings high-light the potential of combining self-supervised learning with multimodal fu-sion for robust and generalizable PET/CT lesion segmentation. Code will be available at https://github.com/RespectKnowledge/AutoPet_2025_BxLSTM_UNET_Segmentation
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