Explicit-implicit priori knowledge-based diffusion model for generative medical image segmentation

doi:10.1016/j.knosys.2024.112426

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Explicit-implicit priori knowledge-based diffusion model for generative medical image segmentation

2024-11-01

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Bangcheng Zhan *

Mingkui Shen

DOI：10.1016/j.knosys.2024.112426

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Abstract

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The diffusion probabilistic model (DPM) has achieved unparalleled results in current image generation tasks, and some recent research works employed it in several computer vision tasks, such as image super-resolution, object detection, etc. Thanks to DPM's superior ability to generate fine-grained details, these research efforts have yielded significant successes. In this paper, we propose a new DPM-based generative medical image segmentation method, named EIDiffuSeg. Specifically, we first construct an explicit-implicit aggregation priori knowledge with directional supervision ability by mining the semantic distribution pattern in the frequency and spatial domains. Then, the explicit-implicit aggregation priori knowledge is integrated into the different encoding stages of the denoising backbone network using a novel unsupervised priori knowledge induction strategy, which can guide the model to generate a segmentation mask of the region of interest directionally from a random inference process. We evaluate our method on three medical image segmentation benchmark datasets with different modalities and achieve the best segmentation results compared to state-of-the-art methods. Especially, compared to several current diffusion-based image segmentation methods, we achieved a 9% Dice improvement in the polyp segmentation benchmark. Our code will be available at https://github.com/Notmezhan/EIDiffuSeg.

Keywords:

Diffusion probabilistic model

Medical image segmentation

Attention mechanism

Deep learning

Generative model