智能引导探头移动，用最少视角实现精准超声成像
SonoSelect: Efficient Ultrasound Perception via Active Probe Exploration
- 根据已有图像动态决定下一步探头位置，减少无效扫描
- 仅用2个视图即达良好器官分类准确率，肾囊肿覆盖率达35.13%
- 适合需要高效超声诊断的临床场景，尤其关注资源受限环境
超声感知通常需通过探头移动获取多个视角以降低诊断歧义、缓解声学遮挡并提升解剖覆盖。然而并非所有视角都同等有效。盲目采集大量视角会引入冗余，增加扫描与处理成本。为此，我们定义了超声主动视角探索任务，并提出SonoSelect——一种基于当前观测自适应引导探头移动的超声专用方法。将每帧2D超声图像融合进3D空间记忆，指导下一探头位置。在此框架下，设计了聚焦更大器官覆盖、更低重建不确定性与更少冗余扫描的专用目标函数。在超声模拟器上的实验表明，SonoSelect仅使用N个视图中的2个即可实现优异的多视角器官分类准确率；对于更具挑战性的肾囊肿检测任务，达到54.56%肾覆盖与35.13%囊肿覆盖，且轨迹始终集中于目标囊肿区域。
原文摘要 · Abstract (English)
Ultrasound perception typically requires multiple scan views through probe movement to reduce diagnostic ambiguity, mitigate acoustic occlusions, and improve anatomical coverage. However, not all probe views are equally informative. Exhaustively acquiring a large number of views can introduce substantial redundancy, increase scanning and processing costs. To address this, we define an active view exploration task for ultrasound and propose SonoSelect, an ultrasound-specific method that adaptively guides probe movement based on current observations. Specifically, we cast ultrasound active view exploration as a sequential decision-making problem. Each new 2D ultrasound view is fused into a 3D spatial memory of the observed anatomy, which guides the next probe position. On top of this formulation, we propose an ultrasound-specific objective that favors probe movements with greater organ coverage, lower reconstruction uncertainty, and less redundant scanning. Experiments on the ultrasound simulator show that SonoSelect achieves promising multi-view organ classification accuracy using only 2 out of N views. Furthermore, for a more difficult kidney cyst detection task, it reaches 54.56% kidney coverage and 35.13% cyst coverage, with short trajectories consistently centered on the target cyst.
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