MFB-SAC: A Multi-Scale Frequency and Boundary-Enhanced SAM for Cell Segmentation

In medical image analysis, precise cell segmentation is crucial for understanding cell morphology and diagnosing diseases. Nevertheless, traditional models typically trained for specific modalities or cell types often fail to generalize to unknown categories. The Segment Anything Model (SAM) was introduced to address this but relies heavily on precise prompts and lacks integration of frequency domain features alongside multi-scale and multi-level information. To overcome these limitations, we propose Multi-scale Frequency and Boundary-enhanced Segment Any Cells (MFB-SAC), which includes a Multi-scale Frequency Convolution (MFC) module for providing multi-scale information and texture detail and a Multi-level Boundary Awareness (MBA) module to enhance boundary retention. Additionally, a Dual-representation Collaborative Attention (DCA) module optimizes feature integration. We tested MFB-SAC on 8 public datasets with various microscopy techniques and cell types. The results reveal that MFB-SAC outperforms the current general and benchmark cell segmentation models. The code is available at https://github.com/Mrliujunwen/SAC.