Huang Zhiyong, Zhao Zhiyu, Yu Zhi, Hou Mingyang, Zhou Shiyao, Wang Jiahong, Yan Yan, Liu Yushi, Gregersen Hans
Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education of China, Chongqing University, Chongqing, 400044, China; School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China.
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China.
Neural Netw. 2025 Jul 29;192:107919. doi: 10.1016/j.neunet.2025.107919.
Medical image segmentation is essential for disease diagnosis and therapy planning, but the complexity of multi-organ structures and blurred skin lesion boundaries poses challenges. CNNs and Transformers are constrained by limited receptive fields and high computational complexity. The state-space model effectively captures long-range dependencies with linear complexity but struggles with local modeling and channel attention.These methods struggle to detect subtle differences in lesion areas, leading to poor performance in medical image segmentation, especially when the lesions are discontinuous or boundaries are unclear.To address these challenges, we propose SCFMUNet, which enhances both local and global modeling across multi-scale features and effectively captures spatial and channel semantics. SCFMUNet integrates three key fusion strategies: 1) At the bottleneck, the multi-scale state-space fusion module is designed to combine convolutions and the SS2D method, processes and fuses the encoder stage features. 2) In the skip connections, the gated adaptive channel mechanism dynamically adjusts the encoder features and fuses them with the decoder stage features using channel-wise addition. 3) In the decoder stages, the spatial channel state-space model performs spatial and channel-level modeling on the fused features from the skip connection stage and the previous decoder layer. Experiments on four public datasets were conducted. On the Synapse dataset and ACDC dataset, our SCFMUNet achieved 82.31 % and 92.14 % on Dice. Compared to state-of-the-art methods, SCFMUNet improves Dice by 0.85 % on Synapse and 1.0 % on ACDC. On the ISIC2017 and ISIC2018 skin lesion datasets, SCFMUNet achieved Dice scores of 90.69 % and 89.69 %, with improvements ranging from 0.5 % to 2 % compared to state-of-the-art methods. Experimental results show that SCFMUNet outperforms state-of-the-art methods on four publicly available biomedical datasets.The source code is publicly available https://github.com/zzzeed/SCFMUNet.
医学图像分割对于疾病诊断和治疗规划至关重要，但多器官结构的复杂性和皮肤病变边界模糊带来了挑战。卷积神经网络（CNNs）和Transformer受限于有限的感受野和高计算复杂度。状态空间模型以线性复杂度有效捕捉长程依赖，但在局部建模和通道注意力方面存在困难。这些方法难以检测病变区域的细微差异，导致医学图像分割性能不佳，尤其是当病变不连续或边界不清楚时。为应对这些挑战，我们提出了SCFMUNet，它在多尺度特征上增强了局部和全局建模，并有效捕捉空间和通道语义。SCFMUNet集成了三种关键融合策略：1）在瓶颈处，多尺度状态空间融合模块旨在结合卷积和SS2D方法，处理并融合编码器阶段的特征。2）在跳跃连接中，门控自适应通道机制动态调整编码器特征，并通过通道相加将其与解码器阶段的特征融合。3）在解码器阶段，空间通道状态空间模型对来自跳跃连接阶段和前一层解码器的融合特征进行空间和通道级建模。我们在四个公共数据集上进行了实验。在Synapse数据集和ACDC数据集上，我们的SCFMUNet在Dice系数上分别达到了82.31%和92.14%。与现有方法相比，SCFMUNet在Synapse数据集上的Dice系数提高了0.85%，在ACDC数据集上提高了1.0%。在ISIC2017和ISIC2018皮肤病变数据集上，SCFMUNet的Dice分数分别为90.69%和89.69%，与现有方法相比提高了0.5%至2%。实验结果表明，SCFMUNet在四个公开的生物医学数据集上优于现有方法。源代码可在https://github.com/zzzeed/SCFMUNet上公开获取。
