Abstract
Medical image segmentation is a crucial method for assisting professionals in diagnosing various diseases through medical imaging. However, various factors such as noise, blurriness, and low contrast often hinder the accurate diagnosis of diseases. While numerous image enhancement techniques can mitigate these issues, they may also alter crucial information needed for accurate diagnosis in the original image. Conventional image fusion strategies, such as feature concatenation can address this challenge. However, they struggle to fully leverage the advantages of both original and enhanced images while suppressing the side effects of the enhancements. To overcome the problem, we propose a dual interactive fusion module (DIFM) that effectively exploits mutual complementary information from the original and enhanced images. DIFM employs cross-attention bidirectionally to simultaneously attend to corresponding spatial information across different images, subsequently refining the complementary features via global spatial attention. This interaction leverages low- to high-level features implicitly associated with diverse structural attributes like edges, blobs, and object shapes, resulting in enhanced features that embody important spatial characteristics. In addition, we introduce a multi-scale boundary loss based on gradient extraction to improve segmentation accuracy at object boundaries. Experimental results on the ACDC and Synapse datasets demonstrate the superiority of the proposed method quantitatively and qualitatively. Code available at: this https URL
Abstract (translated)
医学图像分割是通过医学成像帮助专业人士诊断各种疾病的重要方法。然而，诸如噪声、模糊和低对比度等因素往往阻碍了疾病的准确诊断。尽管有许多图像增强技术可以缓解这些问题，但它们可能会改变原始图像中进行准确诊断所需的关键信息。传统的图像融合策略（如特征级联）可以应对这一挑战，但在充分利用原始图像和增强图像的优势的同时抑制增强所带来的副作用方面存在困难。 为了解决这个问题，我们提出了一种双互动融合模块(Dual Interactive Fusion Module, DIFM)，该模块有效地利用了原始图像和增强图像之间的相互补充信息。DIFM采用双向交叉注意力机制，在不同图像之间同时关注相应的空间信息，并通过全局空间注意进一步精炼互补特征。这种交互作用隐含地关联了低到高级别的特征与边缘、斑点和物体形状等多样结构属性，从而产生了包含重要空间特征的增强特征。 此外，我们还引入了一种基于梯度提取的多尺度边界损失方法，以提高在对象边界处的分割准确性。 在ACDC（自动心脏诊断挑战赛）和Synapse数据集上的实验结果表明，所提出的方法在定量和定性方面都优于现有技术。代码可在以下网址获取：[此链接](https://this-url.com)（请将“this https URL”替换为实际提供的具体URL地址）。
URL
https://arxiv.org/abs/2509.05953
