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

• URL: http://arxiv.org/abs/2406.14186v1
• Date: Thu, 20 Jun 2024 10:46:50 GMT
• Title: CriDiff: Criss-cross Injection Diffusion Framework via Generative Pre-train for Prostate Segmentation
• Authors: Tingwei Liu, Miao Zhang, Leiye Liu, Jialong Zhong, Shuyao Wang, Yongri Piao, Huchuan Lu,
• Abstract summary: CriDiff is a two-stage feature injecting framework with a Crisscross Injection Strategy (CIS) and a Generative Pre-train (GP) approach for prostate segmentation. To effectively learn multi-level of edge features and non-edge features, we proposed two parallel conditioners in the CIS. The GP approach eases the inconsistency between the images features and the diffusion model without adding additional parameters.
• Score: 60.61972883059688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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