Joint Medical Image Enhancement and Segmentation with Diffusion-based Symbiotic Information Interaction

基于扩散模型共生信息交互的医学图像增强与分割联合研究

Abstract: Image quality is critical for accurate medical diagnosis. However, MRI, CT, and ultrasound images are often of low resolution and quality due to cost constraints, complicating the visualization of key anatomical structures and lesions. While such limitations are common in practice, traditional methods treat image enhancement as a separate preprocessing step, failing to fully leverage its potential synergy with image segmentation.

摘要： 图像质量对于准确的医学诊断至关重要。然而，受限于成本因素，MRI、CT 和超声图像往往分辨率较低且质量不佳，这使得关键解剖结构和病灶的观察变得困难。尽管此类局限性在实践中很常见，但传统方法通常将图像增强视为独立的预处理步骤，未能充分利用其与图像分割之间的潜在协同效应。

To address this, we propose DiSIINet (Diffusion-based Symbiotic Information Interaction Network), which is built on the principle that enhancement and segmentation should mutually reinforce each other in a unified model. Based on Denoising Diffusion Implicit Models (DDIM), DiSIINet integrates an enhancement branch and a segmentation branch. These branches interact through a novel Symbiotic Information Interaction (SII) module, which facilitates dynamic, feature-level information exchange via cross-attention during the reverse diffusion process.

为了解决这一问题，我们提出了 DiSIINet（基于扩散的共生信息交互网络），其核心理念是增强与分割任务应在一个统一的模型中相互促进。DiSIINet 基于去噪扩散隐式模型（DDIM），集成了增强分支和分割分支。这两个分支通过一种新颖的共生信息交互（SII）模块进行交互，该模块在反向扩散过程中通过交叉注意力机制促进了动态的特征级信息交换。

This design enables both tasks to iteratively improve each other. The DDIM backbone ensures high-quality output and efficient inference through deterministic sampling. Experiments on multi-modal medical datasets (MRI, CT, ultrasound) show that DiSIINet achieves significant performance improvements compared to sequential or independent enhancement and segmentation approaches. The code is available at: this https URL.

这一设计使得两项任务能够迭代地相互优化。DDIM 主干网络通过确定性采样确保了高质量的输出和高效的推理。在多模态医学数据集（MRI、CT、超声）上的实验表明，与顺序执行或独立的增强与分割方法相比，DiSIINet 实现了显著的性能提升。代码地址：点击此处访问。