Masked Autoencoders (MAEs) have been shown to be effective in pre-training
Vision Transformers (ViTs) for natural and medical image analysis problems. By
reconstructing missing pixel/voxel information in visible patches, a ViT
encoder can aggregate contextual information for downstream tasks. But,
existing MAE pre-training methods, which were specifically developed with the
ViT architecture, lack the ability to capture geometric shape and spatial
information, which is critical for medical image segmentation tasks. In this
paper, we propose a novel extension of known MAEs for self pre-training (i.e.,
models pre-trained on the same target dataset) for 3D medical image
segmentation. (1) We propose a new topological loss to preserve geometric shape
information by computing topological signatures of both the input and
reconstructed volumes, learning geometric shape information. (2) We introduce a
pre-text task that predicts the positions of the centers and eight corners of
3D crops, enabling the MAE to aggregate spatial information. (3) We extend the
MAE pre-training strategy to a hybrid state-of-the-art (SOTA) medical image
segmentation architecture and co-pretrain it alongside the ViT. (4) We develop
a fine-tuned model for downstream segmentation tasks by complementing the
pre-trained ViT encoder with our pre-trained SOTA model. Extensive experiments
on five public 3D segmentation datasets show the effectiveness of our new
approach.

我们提出了一种扩展的 Masked Autoencoders (MAEs) 方法，用于自我预训练 3D 医学图像分割任务，通过引入新的拓扑损失、预测 3D 裁剪区域的位置以及与 Vision Transformers (ViTs) 同时进行预训练，实现对几何形状和空间信息的捕捉，并且在五个公开的 3D 分割数据集上进行了大量实验证明了我们方法的有效性。