超声图像中前列腺癌的配准增强分割方法
Registration-Enhanced Segmentation Method for Prostate Cancer in Ultrasound Images
AI总结：
提出一种基于配准-分割框架的完全自动MRI-TRUS融合方法，直接在TRUS图像中识别前列腺肿瘤，显著提升了分割准确率并减少人工依赖。
摘要：
前列腺癌是男性癌症相关死亡的主要原因，早期发现可大幅提高生存率。尽管MRI-TRUS融合活检通过结合MRI的详细可视化与TRUS的实时引导提供了卓越的准确性，但它是一个复杂且耗时的过程，严重依赖手动标注，从而导致潜在错误。为了解决这些挑战，我们提出了一种基于MRI-TRUS融合的完全自动分割方法，该方法无需手动标注即可直接在TRUS图像中识别前列腺肿瘤。与依赖朴素数据拼接的传统多模态融合方法不同，我们的方法集成了一个配准-分割框架，以对齐并利用MRI与TRUS模态之间的空间信息。这种对齐提高了分割准确性并减少了对人工的依赖。我们的方法在来自Stanford Hospital的1,747名患者数据集上进行了验证，实现了0.212的平均Dice系数，优于仅使用TRUS（0.117）和朴素MRI-TRUS融合（0.132）的方法，具有显著改善（p < 0.01）。该框架展示了降低前列腺癌诊断复杂性的潜力，并提供了一个适用于其他多模态医学成像任务的灵活架构。
英文摘要：
Prostate cancer is a major cause of cancer-related deaths in men, where early detection greatly improves survival rates. Although MRI-TRUS fusion biopsy offers superior accuracy by combining MRI's detailed visualization with TRUS's real-time guidance, it is a complex and time-intensive procedure that relies heavily on manual annotations, leading to potential errors. To address these challenges, we propose a fully automatic MRI-TRUS fusion-based segmentation method that identifies prostate tumors directly in TRUS images without requiring manual annotations. Unlike traditional multimodal fusion approaches that rely on naive data concatenation, our method integrates a registration-segmentation framework to align and leverage spatial information between MRI and TRUS modalities. This alignment enhances segmentation accuracy and reduces reliance on manual effort. Our approach was validated on a dataset of 1,747 patients from Stanford Hospital, achieving an average Dice coefficient of 0.212, outperforming TRUS-only (0.117) and naive MRI-TRUS fusion (0.132) methods, with significant improvements (p $<$ 0.01). This framework demonstrates the potential for reducing the complexity of prostate cancer diagnosis and provides a flexible architecture applicable to other multimodal medical imaging tasks.
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