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}{this https URL}

甲状腺疾病的超声诊断中，通过自动定位超声图像切片到三维形状表示，可以减轻医生的认知负担，并为超声诊断提供方便。使用交叉模态配准和统计形状模型，该方法表明可以在患者特异性的三维解剖学和统计形状模型上准确定位图像切片，并为超声采集过程中的切片定位提供有用的工具。