Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder

TL;DR

This paper introduces GL-MAE, a self-supervised pre-training method for volumetric medical image segmentation that combines global and local masked autoencoding to improve representation stability and clinical context understanding.

Contribution

GL-MAE innovatively integrates global context reconstruction with local masked autoencoding, enhancing volumetric medical image segmentation performance.

Findings

Outperforms state-of-the-art self-supervised methods on multiple datasets.

Effective even with limited annotations.

Improves stability and global context understanding in representations.

Abstract

Masked autoencoder (MAE) is a promising self-supervised pre-training technique that can improve the representation learning of a neural network without human intervention. However, applying MAE directly to volumetric medical images poses two challenges: (i) a lack of global information that is crucial for understanding the clinical context of the holistic data, (ii) no guarantee of stabilizing the representations learned from randomly masked inputs. To address these limitations, we propose the \textbf{G}lobal-\textbf{L}ocal \textbf{M}asked \textbf{A}uto\textbf{E}ncoder (GL-MAE), a simple yet effective self-supervised pre-training strategy. In addition to reconstructing masked local views, as in previous methods, GL-MAE incorporates global context learning by reconstructing masked global views. Furthermore, a complete global view is integrated as an anchor to guide the reconstruction and stabilize the learning process through global-to-global consistency learning and global-to-local consistency learning. Finetuning results on multiple datasets demonstrate the superiority of our method over other state-of-the-art self-supervised algorithms, highlighting its effectiveness on versatile volumetric medical image segmentation tasks, even when annotations are scarce. Our codes and models will be released upon acceptance.