  Due to the scarcity of labeled data, self-supervised learning (SSL) has
gained much attention in 3D medical image segmentation, by extracting semantic
representations from unlabeled data. Among SSL strategies, Masked image
modeling (MIM) has shown effectiveness by reconstructing randomly masked images
to learn detailed representations. However, conventional MIM methods require
extensive training data to achieve good performance, which still poses a
challenge for medical imaging. Since random masking uniformly samples all
regions within medical images, it may overlook crucial anatomical regions and
thus degrade the pretraining efficiency. We propose AnatoMask, a novel MIM
method that leverages reconstruction loss to dynamically identify and mask out
anatomically significant regions to improve pretraining efficacy. AnatoMask
takes a self-distillation approach, where the model learns both how to find
more significant regions to mask and how to reconstruct these masked regions.
To avoid suboptimal learning, Anatomask adjusts the pretraining difficulty
progressively using a masking dynamics function. We have evaluated our method
on 4 public datasets with multiple imaging modalities (CT, MRI, and PET).
AnatoMask demonstrates superior performance and scalability compared to
existing SSL methods. The code is available at
https://github.com/ricklisz/AnatoMask.
