Medical Semantic Segmentation with Diffusion Pretrain

TL;DR

This paper introduces a novel diffusion-based pretraining method with anatomical guidance for 3D medical image segmentation, significantly improving feature generalization and localization accuracy over existing methods.

Contribution

The paper presents a new diffusion pretraining strategy with anatomical guidance tailored for 3D medical images, enhancing segmentation performance and spatial understanding.

Findings

Surpasses existing restorative pretraining methods by 7.5%.

Achieves an average Dice coefficient of 67.8 in non-linear evaluation.

Effectively improves localization and anatomical understanding.

Abstract

Recent advances in deep learning have shown that learning robust feature representations is critical for the success of many computer vision tasks, including medical image segmentation. In particular, both transformer and convolutional-based architectures have benefit from leveraging pretext tasks for pretraining. However, the adoption of pretext tasks in 3D medical imaging has been less explored and remains a challenge, especially in the context of learning generalizable feature representations. We propose a novel pretraining strategy using diffusion models with anatomical guidance, tailored to the intricacies of 3D medical image data. We introduce an auxiliary diffusion process to pretrain a model that produce generalizable feature representations, useful for a variety of downstream segmentation tasks. We employ an additional model that predicts 3D universal body-part coordinates, providing guidance during the diffusion process and improving spatial awareness in generated representations. This approach not only aids in resolving localization inaccuracies but also enriches the model's ability to understand complex anatomical structures. Empirical validation on a 13-class organ segmentation task demonstrate the effectiveness of our pretraining technique. It surpasses existing restorative pretraining methods in 3D medical image segmentation by $7.5\%$, and is competitive with the state-of-the-art contrastive pretraining approach, achieving an average Dice coefficient of 67.8 in a non-linear evaluation scenario.