发表机构
Stanford University School of Medicine（斯坦福大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出MBFormer，一种无位置嵌入的分层Transformer，用于三维超声视频处理，通过注意力机制区分结合与自由微泡，在体内乳腺癌模型中AUC达0.943，优于CNN和SegFormer3D，并支持实时应用。
AI 中文摘要
开发无创超声分子成像（UMI）对于通过临床超声系统进行实时筛查以实现早期癌症检测至关重要。当前技术在准确检测结合到特定生物标志物的靶向微泡（MBs）方面面临挑战，主要原因是未结合的自由漂浮微泡产生的假阳性检测。我们提出了一种用于时间序列视频处理的Transformer模型，以改善结合微泡的区分能力。我们提出了一种分层Transformer，称为MBFormer（微泡Transformer），其特点是采用无位置嵌入的编码器和轻量级解码器。利用三维时空数据中的注意力机制，有效捕获来自结合微泡的静止信号，同时抑制来自未结合微泡的非静止信号。由于与医学成像中的传统分割目标（如器官和肿瘤）相比，微泡表现为相对较小的纹理，我们使用两个分层（每层包含一个注意力块）优化了模型，以处理超声视频数据。网络输出分子信号振幅以可视化精细的微泡纹理。使用体内乳腺癌模型评估性能，并与先前基于CNN的UMI方法和代表性三维Transformer基线SegFormer3D进行比较。MBFormer（AUC = 0.943）在检测结合微泡方面优于CNN（AUC = 0.897）和SegFormer3D（AUC = 0.766）。CNN在心脏腔室中显示出来自自由微泡的残留分子信号，而SegFormer3D未能检测到精细的微泡纹理。总体而言，MBFormer在抑制自由微泡的同时增强了对结合微泡的检测，并实现了16.7至18.1 FPS的帧率，展示了其实时应用的潜力。我们预计这种基于Transformer的UMI模型可以促进临床系统中实时、手持式无创UMI的应用。
英文摘要
Development of nondestructive ultrasound molecular imaging (UMI) is essential for early cancer detection through real-time screening using clinical ultrasound systems. Current techniques face challenges in accurately detecting targeted microbubbles (MBs) bound to specific biomarkers, primarily due to false-positive detections of unbound free-floating MBs. We propose a transformer model for time-series video processing to improve the differentiation of bound MBs. We propose a hierarchical transformer, termed MBFormer (microbubble transformer), featuring a positional-embedding-free encoder and a lightweight decoder. Leveraging attention within 3D spatio-temporal data to effectively capture stationary signals from bound MBs while suppressing nonstationary signals from unbound MBs. Since MBs appears as relatively small textures compared with conventional segmentation targets in medical imaging, such as organs and tumors, we optimized the model with two hierarchical layers, each with an attention block, to process ultrasound video data. The network outputs the molecular signal amplitude to visualize fine MB textures. Performance was evaluated using an in vivo breast cancer model, compared against a prior CNN-based UMI method and SegFormer3D baseline, a representative 3D transformer. MBFormer (AUC = 0.943) outperformed both CNN (AUC = 0.897) and SegFormer3D (AUC = 0.766) in detecting bound MBs. The CNN showed residual molecular signal from free MBs in the cardiac chambers, whereas SegFormer3D failed to detect fine MB textures. Overall, MBFormer demonstrated enhanced detection of bound MBs while suppressing free MBs and achieved a frame rate of 16.7 to 18.1 FPS, demonstrating its potential for real-time application. We anticipate that this transformer-based UMI model can facilitate real-time, free-hand nondestructive UMI in clinical systems.
