Medical Semantic Segmentation with Diffusion Pretrain
Journal:
arXiv
Published Date:
Jan 31, 2025
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.
