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