ACM-UNet: Adaptive Integration of CNNs and Mamba for Efficient Medical Image Segmentation

TL;DR

ACM-UNet is a versatile medical image segmentation framework that effectively combines pretrained CNNs and Mamba SSMs through a lightweight adapter, achieving state-of-the-art results with high efficiency.

Contribution

It introduces a novel adapter mechanism to integrate CNNs and Mamba models into a UNet-like architecture for improved segmentation performance.

Findings

Achieves 85.12% Dice Score on Synapse dataset.

Reaches 13.89mm HD95 with 17.93G FLOPs.

Outperforms existing methods on benchmark datasets.

Abstract

The U-shaped encoder-decoder architecture with skip connections has become a prevailing paradigm in medical image segmentation due to its simplicity and effectiveness. While many recent works aim to improve this framework by designing more powerful encoders and decoders, employing advanced convolutional neural networks (CNNs) for local feature extraction, Transformers or state space models (SSMs) such as Mamba for global context modeling, or hybrid combinations of both, these methods often struggle to fully utilize pretrained vision backbones (e.g., ResNet, ViT, VMamba) due to structural mismatches. To bridge this gap, we introduce ACM-UNet, a general-purpose segmentation framework that retains a simple UNet-like design while effectively incorporating pretrained CNNs and Mamba models through a lightweight adapter mechanism. This adapter resolves architectural incompatibilities and enables the model to harness the complementary strengths of CNNs and SSMs-namely, fine-grained local detail extraction and long-range dependency modeling. Additionally, we propose a hierarchical multi-scale wavelet transform module in the decoder to enhance feature fusion and reconstruction fidelity. Extensive experiments on the Synapse and ACDC benchmarks demonstrate that ACM-UNet achieves state-of-the-art performance while remaining computationally efficient. Notably, it reaches 85.12% Dice Score and 13.89mm HD95 on the Synapse dataset with 17.93G FLOPs, showcasing its effectiveness and scalability. Code is available at: https://github.com/zyklcode/ACM-UNet.