Transformer Lesion Tracker

TL;DR

This paper introduces a transformer-based method for automated lesion tracking that combines global and local information, integrating anatomical data to improve accuracy in clinical lesion progression assessment.

Contribution

The work presents a novel transformer architecture with cross attention, anatomical attention, and feature selection strategies for improved lesion tracking accuracy.

Findings

Achieved at least 14.3% reduction in Euclidean center error compared to SOTA.

Demonstrated superior performance on a public dataset.

Enhanced feature extraction by integrating global, local, and anatomical information.

Abstract

Evaluating lesion progression and treatment response via longitudinal lesion tracking plays a critical role in clinical practice. Automated approaches for this task are motivated by prohibitive labor costs and time consumption when lesion matching is done manually. Previous methods typically lack the integration of local and global information. In this work, we propose a transformer-based approach, termed Transformer Lesion Tracker (TLT). Specifically, we design a Cross Attention-based Transformer (CAT) to capture and combine both global and local information to enhance feature extraction. We also develop a Registration-based Anatomical Attention Module (RAAM) to introduce anatomical information to CAT so that it can focus on useful feature knowledge. A Sparse Selection Strategy (SSS) is presented for selecting features and reducing memory footprint in Transformer training. In addition, we use a global regression to further improve model performance. We conduct experiments on a public dataset to show the superiority of our method and find that our model performance has improved the average Euclidean center error by at least 14.3% (6mm vs. 7mm) compared with the state-of-the-art (SOTA). Code is available at https://github.com/TangWen920812/TLT.