Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection

TL;DR

This paper introduces a domain alignment approach with regional facial extraction and artefact augmentation for cephalometric landmark detection, achieving top results in the MICCAI 2024 challenge.

Contribution

It presents a novel domain alignment strategy combined with data augmentation techniques for improved landmark detection accuracy.

Findings

Best MRE of 1.186mm in challenge

Third place with 82.04% SDR at 2mm

Effective domain adaptation for X-ray images

Abstract

Cephalometric Landmark Detection is the process of identifying key areas for cephalometry. Each landmark is a single GT point labelled by a clinician. A machine learning model predicts the probability locus of a landmark represented by a heatmap. This work, for the 2024 CL-Detection MICCAI Challenge, proposes a domain alignment strategy with a regional facial extraction module and an X-ray artefact augmentation procedure. The challenge ranks our method's results as the best in MRE of 1.186mm and third in the 2mm SDR of 82.04% on the online validation leaderboard. The code is available at https://github.com/Julian-Wyatt/OptimisingfortheUnknown.