CriDiff: Criss-cross Injection Diffusion Framework via Generative Pre-train for Prostate Segmentation

TL;DR

CriDiff introduces a novel two-stage framework with a criss-cross injection strategy and generative pre-training to improve prostate segmentation by effectively learning and injecting multi-level edge and non-edge features, addressing domain gaps.

Contribution

The paper proposes CriDiff, a two-stage feature injection framework with a criss-cross strategy and generative pre-training, enhancing diffusion models for prostate segmentation.

Findings

Achieves state-of-the-art performance on four benchmark datasets.

Effectively learns multi-level edge and non-edge features.

Demonstrates significant improvement over existing methods.

Abstract

Recently, the Diffusion Probabilistic Model (DPM)-based methods have achieved substantial success in the field of medical image segmentation. However, most of these methods fail to enable the diffusion model to learn edge features and non-edge features effectively and to inject them efficiently into the diffusion backbone. Additionally, the domain gap between the images features and the diffusion model features poses a great challenge to prostate segmentation. In this paper, we proposed CriDiff, a two-stage feature injecting framework with a Crisscross Injection Strategy (CIS) and a Generative Pre-train (GP) approach for prostate segmentation. The CIS maximizes the use of multi-level features by efficiently harnessing the complementarity of high and low-level features. To effectively learn multi-level of edge features and non-edge features, we proposed two parallel conditioners in the CIS: the Boundary Enhance Conditioner (BEC) and the Core Enhance Conditioner (CEC), which discriminatively model the image edge regions and non-edge regions, respectively. Moreover, the GP approach eases the inconsistency between the images features and the diffusion model without adding additional parameters. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the proposed method and achieve state-of-the-art performance on four evaluation metrics.