Revisiting MAE pre-training for 3D medical image segmentation
This work improves 3D medical image segmentation for clinical applications by setting a new state-of-the-art, though it is incremental as it builds on existing MAE and nnU-Net frameworks.
The paper tackled the limited adoption of self-supervised learning in 3D medical image segmentation by addressing pitfalls like small datasets and inadequate architectures, resulting in a model that outperforms previous SSL methods and the nnU-Net baseline by an average of about 3 Dice points.
Self-Supervised Learning (SSL) presents an exciting opportunity to unlock the potential of vast, untapped clinical datasets, for various downstream applications that suffer from the scarcity of labeled data. While SSL has revolutionized fields like natural language processing and computer vision, its adoption in 3D medical image computing has been limited by three key pitfalls: Small pre-training dataset sizes, architectures inadequate for 3D medical image analysis, and insufficient evaluation practices. In this paper, we address these issues by i) leveraging a large-scale dataset of 39k 3D brain MRI volumes and ii) using a Residual Encoder U-Net architecture within the state-of-the-art nnU-Net framework. iii) A robust development framework, incorporating 5 development and 8 testing brain MRI segmentation datasets, allowed performance-driven design decisions to optimize the simple concept of Masked Auto Encoders (MAEs) for 3D CNNs. The resulting model not only surpasses previous SSL methods but also outperforms the strong nnU-Net baseline by an average of approximately 3 Dice points setting a new state-of-the-art. Our code and models are made available here.