Anatomical Landmarks Localization for 3D Foot Point Clouds

TL;DR

This paper presents a deformation-based method for accurately localizing 3D anatomical landmarks on foot point clouds, improving robustness and performance over existing techniques.

Contribution

It introduces a novel non-rigid deformation approach with alignment and smoothness constraints for automated 3D anatomical landmark prediction.

Findings

Method outperforms state-of-the-art in most cases

Demonstrates robustness on a new dataset

Effective in localizing foot anatomical landmarks

Abstract

3D anatomical landmarks play an important role in health research. Their automated prediction/localization thus becomes a vital task. In this paper, we introduce a deformation method for 3D anatomical landmarks prediction. It utilizes a source model with anatomical landmarks which are annotated by clinicians, and deforms this model non-rigidly to match the target model. Two constraints are introduced in the optimization, which are responsible for alignment and smoothness, respectively. Experiments are performed on our dataset and the results demonstrate the robustness of our method, and show that it yields better performance than the state-of-the-art techniques in most cases.