Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US). Longitudinal nodule tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology. This task, however, imposes a substantial cognitive load on clinicians due to the inherent challenge of maintaining a mental 3D reconstruction of the organ. We thus present a framework for automated US image slice localization within a 3D shape representation to ease how such sonographic diagnoses are carried out. Our proposed method learns a common latent embedding space between US image patches and the 3D surface of an individual's thyroid shape, or a statistical aggregation in the form of a statistical shape model (SSM), via contrastive metric learning. Using cross-modality registration and Procrustes analysis, we leverage features from our model to register US slices to a 3D mesh representation of the thyroid shape. We demonstrate that our multi-modal registration framework can localize images on the 3D surface topology of a patient-specific organ and the mean shape of an SSM. Experimental results indicate slice positions can be predicted within an average of 1.2 mm of the ground-truth slice location on the patient-specific 3D anatomy and 4.6 mm on the SSM, exemplifying its usefulness for slice localization during sonographic acquisitions. Code is publically available: \href{https://github.com/vuenc/slice-to-shape}{https://github.com/vuenc/slice-to-shape}

翻译：甲状腺疾病最常通过高分辨率超声诊断。纵向结节追踪是监测病理性甲状腺形态变化的关键诊断方案，但由于需要在大脑中维持器官的三维重建，该任务给临床医生带来了显著的认知负荷。为此，我们提出了一种在三维形状表示中实现超声图像切片自动定位的框架，以简化此类超声诊断流程。该方法通过对比度量学习，在超声图像块与个体甲状腺形状的三维表面之间（或统计形状模型（SSM）形式的统计聚合结果）学习共同的潜在嵌入空间。利用跨模态注册和Procrustes分析，我们提取模型特征将超声切片配准至甲状腺形状的三维网格表示。实验证明，该多模态配准框架能够在患者特定器官的三维表面拓扑结构及SSM平均形状上定位图像切片。结果表明，在患者特定三维解剖结构上，切片位置预测与真实位置的平均误差为1.2毫米，在SSM上为4.6毫米，验证了其在超声采集过程中进行切片定位的实用性。代码已开源：\href{https://github.com/vuenc/slice-to-shape}{https://github.com/vuenc/slice-to-shape}