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 and expertly annotated datasets, making them unsuitable for longitudinal AS analysis, where multiple time points and a scarcity of expert labels hinder their 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 delination. Moreover, we introduce 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.

翻译：主动监测（AS）是管理低危和中危前列腺癌（PCa）的一种治疗选择，旨在避免过度治疗，同时通过系列MRI和临床随访监测疾病进展。精确的前列腺分割是实现该过程自动化的关键初步步骤，有助于PCa的自动检测与诊断。然而，现有的深度学习分割模型通常在单时间点且经专家标注的数据集上训练，使其不适用于纵向AS分析——多时间点数据与专家标注稀缺的问题阻碍了模型的有效微调。为应对这些挑战，我们提出MambaX-Net，一种新颖的半监督双扫描3D分割架构，该架构通过利用当前时间点t的MRI及前一时间点的对应分割掩码来计算时间点t的分割结果。我们引入了两个新组件：（i）Mamba增强交叉注意力模块，将Mamba块集成至交叉注意力中，以高效捕捉时间演化特征与长程空间依赖关系；（ii）形状提取器模块，将前一时间点的分割掩码编码为潜在解剖表征，以实现精细化的前列腺分区轮廓划分。此外，我们提出一种半监督自训练策略，利用基于预训练nnU-Net生成的伪标签进行学习，从而在无需专家标注的情况下实现有效训练。MambaX-Net在纵向AS数据集上进行了评估，结果表明其显著优于当前最先进的基于U-Net和Transformer的模型，即使在有限且含噪声的数据上训练，也能实现更优的前列腺分区分割效果。