DATR: Domain-adaptive transformer for multi-domain landmark detection

TL;DR

This paper introduces DATR, a universal transformer-based model capable of multi-domain anatomical landmark detection across different medical imaging datasets, outperforming previous CNN-based methods.

Contribution

The paper presents the first transformer-based universal model for multi-domain landmark detection, incorporating a domain-adaptive transformer and a lightweight guidance network.

Findings

Achieves state-of-the-art performance on three X-ray datasets.

Outperforms previous CNN-based landmark detection models.

Effectively detects landmarks across head, hand, and chest anatomies.

Abstract

Accurate anatomical landmark detection plays an increasingly vital role in medical image analysis. Although existing methods achieve satisfying performance, they are mostly based on CNN and specialized for a single domain say associated with a particular anatomical region. In this work, we propose a universal model for multi-domain landmark detection by taking advantage of transformer for modeling long dependencies and develop a domain-adaptive transformer model, named as DATR, which is trained on multiple mixed datasets from different anatomies and capable of detecting landmarks of any image from those anatomies. The proposed DATR exhibits three primary features: (i) It is the first universal model which introduces transformer as an encoder for multi-anatomy landmark detection; (ii) We design a domain-adaptive transformer for anatomy-aware landmark detection, which can be effectively extended to any other transformer network; (iii) Following previous studies, we employ a light-weighted guidance network, which encourages the transformer network to detect more accurate landmarks. We carry out experiments on three widely used X-ray datasets for landmark detection, which have 1,588 images and 62 landmarks in total, including three different anatomies (head, hand, and chest). Experimental results demonstrate that our proposed DATR achieves state-of-the-art performances by most metrics and behaves much better than any previous convolution-based models. The code will be released publicly.