  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.
