发表机构
Newcastle University（纽卡斯尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出首个针对高频微超声前列腺分割的零样本流程，基于预训练的MedSAM模型，结合CLAHE、二值膨胀与傅里叶平滑优化，在测试集上显著降低边界误差，分割效果接近非专家水平且无需额外数据。
AI 中文摘要
前列腺癌每80秒夺走一条生命，早期检测对阻止疾病进展至关重要，PSA密度计算和活检决策均依赖于前列腺腺体的精确边界。传统6-12 MHz超声会模糊该边界，每3例高危癌症中就有1例被漏诊；29 MHz微超声的分辨率提升了3倍，但会产生密集的声学散斑而掩盖前列腺外壁，对于同一图像，两名临床医生绘制的轮廓面积差异超过10%。有监督方法成本高昂且在不同超声设备间的泛化性差。能否用基础模型在无训练数据的情况下实现前列腺分割？我们提出了针对该模态的首个零样本流程：MedSAM（在超过150万张医学图像上预训练）定位前列腺；随后应用CLAHE增强外壁清晰度，二值膨胀恢复缺失像素，傅里叶平滑（4个模式，s=1.05）优化边界。MedSAM需要空间提示，因此我们在Micro-Ultrasound Prostate Segmentation数据集（共2621张切片）的75名患者上评估了边界框和点点击两种策略。在20名患者的保留测试集上，该流程将平均边界距离误差降低了45%（Dice从0.749±0.043提升至0.865±0.029；HD95从217.2±36.9降至120.1±26.1像素），整个队列的Dice达0.859。其平均重叠度与三名非专家评分者组无显著差异（p>0.19），同时分割一致性提升了38%-52%（患者间标准差更低）。无论放置位置如何，点点击提示均失效（最佳Dice=0.350），因为散斑无法提供稳定的局部对比度；仅需一个近似边界框即可部署，因此任何诊所都无需数据收集、标注或重新训练即可使用。
英文摘要
Prostate cancer claims a life every 80 seconds. Early detection is needed to prevent disease progression, and both PSA density calculation and biopsy decisions rely on knowing the exact boundary of the gland. Conventional ultrasound at 6-12 MHz blurs this boundary, missing one in three high-risk cancers. Micro-ultrasound (29 MHz) improves resolution threefold but introduces dense acoustic speckle that obscures the outer wall; given the same image, two clinicians draw outlines differing by over 10% in area. Supervised methods are costly and generalise poorly across scanners. Can a foundation model segment the prostate with no training data? We present the first zero-shot pipeline for this modality: MedSAM, pre-trained on over 1.5 million medical images, localises the prostate; we then apply CLAHE to sharpen the outer wall, binary dilation to recover missed pixels, and Fourier smoothing (4 modes, s=1.05) to refine the boundary. MedSAM requires a spatial prompt, so we evaluate bounding-box and point-click strategies across 75 patients of the Micro-Ultrasound Prostate Segmentation dataset (2,621 slices). On the 20-patient held-out test set, the pipeline reduces mean boundary-distance error by 45% (Dice 0.749+/-0.043 to 0.865+/-0.029; HD95 217.2+/-36.9 to 120.1+/-26.1 px), reaching Dice 0.859 across the cohort. Its mean overlap shows no significant difference from the three non-expert rater groups (p>0.19), while segmenting 38-52% more consistently (lower inter-patient standard deviation). Point-click prompts fail regardless of placement (best Dice=0.350), because speckle gives no stable local contrast. Only an approximate bounding box is required, so any clinic can deploy it without data collection, annotation, or retraining.
