Title: Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning Show Chinese title

Title: 自我是最佳学习者：通过协作去噪和分割学习实现无CT超低剂量PET器官分割

Abstract: Organ segmentation in Positron Emission Tomography (PET) plays a vital role in cancer quantification. Low-dose PET (LDPET) provides a safer alternative by reducing radiation exposure. However, the inherent noise and blurred boundaries make organ segmentation more challenging. Additionally, existing PET organ segmentation methods rely on coregistered Computed Tomography (CT) annotations, overlooking the problem of modality mismatch. In this study, we propose LDOS, a novel CT-free ultra-LDPET organ segmentation pipeline. Inspired by Masked Autoencoders (MAE), we reinterpret LDPET as a naturally masked version of Full-Dose PET (FDPET). LDOS adopts a simple yet effective architecture: a shared encoder extracts generalized features, while task-specific decoders independently refine outputs for denoising and segmentation. By integrating CT-derived organ annotations into the denoising process, LDOS improves anatomical boundary recognition and alleviates the PET/CT misalignments. Experiments demonstrate that LDOS achieves state-of-the-art performance with mean Dice scores of 73.11% (18F-FDG) and 73.97% (68Ga-FAPI) across 18 organs in 5% dose PET. Our code will be available at https://github.com/yezanting/LDOS. Show Chinese abstract

Abstract: 正电子发射断层扫描（PET）中的器官分割在癌症量化中起着至关重要的作用。低剂量PET（LDPET）通过减少辐射暴露提供了一种更安全的替代方案。然而，固有的噪声和模糊的边界使得器官分割更具挑战性。此外，现有的PET器官分割方法依赖于配准的计算机断层扫描（CT）注释，忽视了模态不匹配的问题。在本研究中，我们提出了LDOS，一种新的无CT的超低剂量PET器官分割流程。受掩码自编码器（MAE）的启发，我们将LDPET重新解释为全剂量PET（FDPET）的自然掩码版本。LDOS采用了一个简单而有效的架构：共享编码器提取通用特征，而任务特定解码器独立优化输出以进行去噪和分割。通过将CT衍生的器官注释整合到去噪过程中，LDOS提高了解剖边界识别并缓解了PET/CT的错位问题。实验表明，LDOS在5%剂量PET的18个器官上实现了最先进的性能，平均Dice分数分别为73.11%（18F-FDG）和73.97%（68Ga-FAPI）。我们的代码将在https://github.com/yezanting/LDOS上提供。