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Recently, denoising diffusion probabilistic models (DDPM) have been appliedto image segmentation by generating segmentation masks conditioned on images,while the applications were mainly limited to 2D networks without exploitingpotential benefits from the 3D formulation. In this work, we studied theDDPM-based segmentation model for 3D multiclass segmentation on two largemulticlass data sets (prostate MR and abdominal CT). We observed that thedifference between training and test methods led to inferior performance forexisting DDPM methods. To mitigate the inconsistency, we proposed a recyclingmethod which generated corrupted masks based on the model's prediction at aprevious time step instead of using ground truth. The proposed method achievedstatistically significantly improved performance compared to existing DDPMs,independent of a number of other techniques for reducing train-testdiscrepancy, including performing mask prediction, using Dice loss, andreducing the number of diffusion time steps during training. The performance ofdiffusion models was also competitive and visually similar tonon-diffusion-based U-net, within the same compute budget. The JAX-baseddiffusion framework has been released athttps://github.com/mathpluscode/ImgX-DiffSeg.

Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation