MambaX-Net：双输入Mamba增强交叉注意力网络用于纵向MRI分割
MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal Prostate MRI Segmentation
AI总结：
MambaX-Net通过双输入和Mamba增强的交叉注意力模块，实现纵向MRI前列腺分割的高效准确分割。
摘要：
主动监测（AS）是管理低至中等风险前列腺癌（PCa）的一种治疗选择，旨在避免过度治疗，同时通过连续MRI和临床随访监测疾病进展。准确的前列腺分割是自动化此过程的重要初步步骤，使能够自动检测和诊断PCa。然而，现有的深度学习分割模型通常是在单时间点和专家标注的数据集上训练的，使其不适合纵向AS分析，其中多个时间点和专家标签的稀缺性阻碍了其有效微调。为了解决这些挑战，我们提出了MambaX-Net，一种新的半监督、双扫描3D分割架构，通过利用MRI和前一时间点的分割掩码来计算时间点t的分割。我们引入了两个新组件：（i）Mamba增强的交叉注意力模块，将Mamba块整合到交叉注意力中，以高效捕捉时间演变和长距离空间依赖性；（ii）形状提取器模块，将前一时间点的分割掩码编码为潜在的解剖表示以实现精确区域划分。此外，我们引入了一种半监督自训练策略，利用从预训练的nnU-Net生成的伪标签，实现无需专家标注的有效学习。MambaX-Net在纵向AS数据集上进行了评估，结果表明，它显著优于最先进的U-Net和基于Transformer的模型，即使在训练数据有限且嘈杂的情况下，也能实现优越的前列腺区域分割。
英文摘要：
Active Surveillance (AS) is a treatment option for managing low and intermediate-risk prostate cancer (PCa), aiming to avoid overtreatment while monitoring disease progression through serial MRI and clinical follow-up. Accurate prostate segmentation is an important preliminary step for automating this process, enabling automated detection and diagnosis of PCa. However, existing deep-learning segmentation models are often trained on single-time-point, expertly annotated datasets, making them unsuitable for longitudinal AS analysis, where multiple time points and a scarcity of expert labels hinder effective fine-tuning. To address these challenges, we propose MambaX-Net, a novel semi-supervised, dual-scan 3D segmentation architecture that computes the segmentation for time point t by leveraging the MRI and the corresponding segmentation mask from the previous time point. We introduce two new components: (i) a Mamba-enhanced Cross-Attention Module, which integrates the Mamba block into cross-attention to efficiently capture temporal evolution and long-range spatial dependencies, and (ii) a Shape Extractor Module that encodes the previous segmentation mask into a latent anatomical representation for refined zone delineation. Moreover, we use a semi-supervised self-training strategy that leverages pseudo-labels generated from a pre-trained nnU-Net, enabling effective learning without expert annotations. MambaX-Net was evaluated on a longitudinal AS dataset, and results showed that it significantly outperforms state-of-the-art U-Net and Transformer-based models, achieving superior prostate zone segmentation even when trained on limited and noisy data.
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。
