SliceMamba with Neural Architecture Search for Medical Image Segmentation.

Despite the progress made in Mamba-based medical image segmentation models, existing methods utilizing unidirectional or multi-directional feature scanning mechanisms struggle to effectively capture dependencies between neighboring positions, limiting the discriminant representation learning of local features. These local features are crucial for medical image segmentation as they provide critical structural information about lesions and organs. To address this limitation, we propose SliceMamba, a simple yet effective locally sensitive Mamba-based medical image segmentation model. SliceMamba features an efficient Bidirectional Slicing and Scanning (BSS) module, which performs bidirectional feature slicing and employs varied scanning mechanisms for sliced features with distinct shapes. This design keeps spatially adjacent features close in the scan sequence, preserving the local structure of the image and enhancing segmentation performance. Additionally, to fit the varying sizes and shapes of lesions and organs, we introduce an Adaptive Slicing Search method that automatically identifies the optimal feature slicing method based on the characteristics of the target data. Extensive experiments on two skin lesion datasets (ISIC2017 and ISIC2018), two polyp segmentation datasets (Kvasir and ClinicDB), one ultra-wide field retinal hemorrhage segmentation dataset (UWF-RHS), and one multi-organ segmentation dataset (Synapse) demonstrate the effectiveness of our method.