Integrating components from convolutional neural networks and state space
models in medical image segmentation presents a compelling approach to enhance
accuracy and efficiency. We introduce Mamba HUNet, a novel architecture
tailored for robust and efficient segmentation tasks. Leveraging strengths from
Mamba UNet and the lighter version of Hierarchical Upsampling Network (HUNet),
Mamba HUNet combines convolutional neural networks local feature extraction
power with state space models long range dependency modeling capabilities. We
first converted HUNet into a lighter version, maintaining performance parity
and then integrated this lighter HUNet into Mamba HUNet, further enhancing its
efficiency. The architecture partitions input grayscale images into patches,
transforming them into 1D sequences for processing efficiency akin to Vision
Transformers and Mamba models. Through Visual State Space blocks and patch
merging layers, hierarchical features are extracted while preserving spatial
information. Experimental results on publicly available Magnetic Resonance
Imaging scans, notably in Multiple Sclerosis lesion segmentation, demonstrate
Mamba HUNet's effectiveness across diverse segmentation tasks. The model's
robustness and flexibility underscore its potential in handling complex
anatomical structures. These findings establish Mamba HUNet as a promising
solution in advancing medical image segmentation, with implications for
improving clinical decision making processes.

提出了一种新的架构 Mamba HUNet，该架构结合了卷积神经网络的局部特征提取能力和状态空间模型的长程依赖建模能力，通过将输入灰度图像分割为补丁并转换为 1D 序列，提取层次特征并保留空间信息，实验结果表明 Mamba HUNet 在医学图像分割任务中具有良好的效果，特别是在多发性硬化症损伤分割方面表现出强大的鲁棒性和灵活性，对改善临床决策过程具有潜在的推动作用。