On the Localization of Ultrasound Image Slices within Point Distribution Models

TL;DR

This paper introduces a contrastive learning framework for localizing ultrasound image slices within a 3D thyroid shape model, improving accuracy and aiding longitudinal nodule tracking in thyroid diagnostics.

Contribution

It presents a novel multi-modal registration method that aligns US slices with 3D thyroid models using contrastive metric learning and shape registration techniques.

Findings

Slice localization within 1.2 mm on patient-specific anatomy

Localization within 4.6 mm on statistical shape model

Framework facilitates longitudinal thyroid nodule monitoring

Abstract

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}