  Medical image segmentation has been significantly advanced with the rapid
development of deep learning (DL) techniques. Existing DL-based segmentation
models are typically discriminative; i.e., they aim to learn a mapping from the
input image to segmentation masks. However, these discriminative methods
neglect the underlying data distribution and intrinsic class characteristics,
suffering from unstable feature space. In this work, we propose to complement
discriminative segmentation methods with the knowledge of underlying data
distribution from generative models. To that end, we propose a novel hybrid
diffusion framework for medical image segmentation, termed HiDiff, which can
synergize the strengths of existing discriminative segmentation models and new
generative diffusion models. HiDiff comprises two key components:
discriminative segmentor and diffusion refiner. First, we utilize any
conventional trained segmentation models as discriminative segmentor, which can
provide a segmentation mask prior for diffusion refiner. Second, we propose a
novel binary Bernoulli diffusion model (BBDM) as the diffusion refiner, which
can effectively, efficiently, and interactively refine the segmentation mask by
modeling the underlying data distribution. Third, we train the segmentor and
BBDM in an alternate-collaborative manner to mutually boost each other.
Extensive experimental results on abdomen organ, brain tumor, polyps, and
retinal vessels segmentation datasets, covering four widely-used modalities,
demonstrate the superior performance of HiDiff over existing medical
segmentation algorithms, including the state-of-the-art transformer- and
diffusion-based ones. In addition, HiDiff excels at segmenting small objects
and generalizing to new datasets. Source codes are made available at
https://github.com/takimailto/HiDiff.
