# LM-UNet: Lightweight Mamba-UNet Prostate MRI image segmentation network

## TL;DR

This paper introduces LM-UNet, a new lightweight network for more accurate prostate MRI image segmentation with improved boundary detection.

## Contribution

The novel LM-UNet combines PV-Mamba and EMA modules with edge feature fusion to enhance segmentation accuracy while reducing model complexity.

## Key findings

- LM-UNet achieves better segmentation accuracy with fewer parameters compared to seven existing methods.
- The model improves lesion margin precision on the PROMISE12 dataset.
- Multi-level fusion reduces semantic discrepancies between encoder and decoder features.

## Abstract

Accurate segmentation of lesions in prostate magnetic resonance images (MRI) is important for assessing patient health and personalized treatment in the clinic. However, the traditional UNet segmentation network has low segmentation accuracy because of the fuzzy boundary and low contrast. Therefore, we propose a Lightweight Mamba-UNet (LM-UNet) prostate MRI image segmentation method. Initially, the encoder-decoder backbone structure consists of parallel vision mamba (PV-Mamba) and efficient multi-scale attention (EMA). The number of model parameters is reduced by constructing PV-Mamba while extracting the correlation between features over long distances. The EMA is then used to learn different spatial features in groups and construct cross-spatial information aggregation methods for richer feature aggregation. Subsequently, we construct the edge feature extraction (EFE) and the edge feature fusion (EFF) to achieve different levels of feature fusion in the encoder. Ultimately, we suggest a multi-stage and multi-level skip connections (MMSC) to achieve multi-level fusion between the encoder and decoder, there reducing semantic discrepancies between contextual features and improving segmentation accuracy. Experimental results demonstrate that on the PROMISE12 dataset, LM-UNet outperforms seven comparative segmentation methods in terms of parameter count, computational memory requirements, and precise segmentation of lesion margins.