Medical image segmentation involves identifying and separating object instances in a medical image to delineate various tissues and structures, a task complicated by the significant variations in size, shape, and density of these features. Convolutional neural networks (CNNs) have traditionally been used for this task but have limitations in capturing long-range dependencies. Transformers, equipped with self-attention mechanisms, aim to address this problem. However, in medical image segmentation it is beneficial to merge both local and global features to effectively integrate feature maps across various scales, capturing both detailed features and broader semantic elements for dealing with variations in structures. In this paper, we introduce MSA2Net, a new deep segmentation framework featuring an expedient design of skip-connections. These connections facilitate feature fusion by dynamically weighting and combining coarse-grained encoder features with fine-grained decoder feature maps. Specifically, we propose a Multi-Scale Adaptive Spatial Attention Gate (MASAG), which dynamically adjusts the receptive field (Local and Global contextual information) to ensure that spatially relevant features are selectively highlighted while minimizing background distractions. Extensive evaluations involving dermatology, and radiological datasets demonstrate that our MSA2Net outperforms state-of-the-art (SOTA) works or matches their performance. The source code is publicly available at https://github.com/xmindflow/MSA-2Net.

翻译：医学图像分割旨在识别并分离医学图像中的目标实例，以勾画不同组织与结构，该任务因目标特征在尺寸、形状及密度上的显著差异而变得复杂。传统上，卷积神经网络（CNN）被用于此项任务，但其在捕捉长程依赖关系方面存在局限。配备自注意力机制的Transformer旨在解决这一问题。然而，在医学图像分割中，融合局部与全局特征有利于跨尺度有效整合特征图，从而捕获细节特征与更广泛的语义信息，以应对结构变化。本文提出MSA2Net，一种具有高效跳跃连接设计的新型深度分割框架。这些连接通过动态加权和组合粗粒度编码器特征与细粒度解码器特征图，促进特征融合。具体而言，我们提出一种多尺度自适应空间注意力门（MASAG），其动态调整感受野（局部与全局上下文信息），以确保空间相关特征被选择性增强，同时最小化背景干扰。在皮肤病学及放射学数据集上的广泛评估表明，我们的MSA2Net优于或匹配当前最先进（SOTA）方法。源代码公开于https://github.com/xmindflow/MSA-2Net。