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.Comment: Accepted MICCAI 202