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收稿日期：2024-09-19，
修回日期：2025-02-14，
录用日期：2025-03-03，
网络出版日期：2025-03-04，
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状态空间模型（state-space model，SSM）在长序列计算效率方面表现优异。2024年基于SSM的具有选择机制和硬件感知状态扩展的Mamba模型问世，状态空间模型成为新的备受瞩目的人工智能架构，其性能可能超过Transformer。为了充分了解状态空间模型在医学图像处理领域的研究和应用，本文进行了全面的调查，首先对状态空间模型的发展历程和各种基于SSM的基础模型进行总结，然后按照图像分割、分类、配准和融合、重建，以及疾病预测、医学图像合成、放射治疗剂量预测任务进行分类研究，探讨了每种任务中SSM模型的改进和应用，最后讨论了状态空间模型面临的挑战和今后的研究方向。此外，本文讨论的研究及其开源实现汇编在了GitHub中，地址为：
https：//github.com/wyl32123/ssm-medical-paper/tree/main
https://github.com/wyl32123/ssm-medical-paper/tree/main
。
The State-Space Model（SSM）has excelled in computational efficiency over long sequences and with the introduction of the SSM-based Mamba model in 2024 the SSM has become the new high-profile AI architecture.It combines the strengths of CNN and Transformer technologies to efficiently capture local information and remote dependencies while maintaining linear complexity in computation，and has great potential for processing high-resolution data or long-term sequential data.To fully understand the research and application of state space models in the field of medical image processing，we conduct a comprehensive survey to sort out the development lineage，key models and application scenarios of SSM models in the field of medical image processing.First， we summarise the history of state space models， describing the evolution of state space models from SSM-
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Mamba-
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Mamba-2. Focusing on SSM and Mamba， we describe the basics of SSM and enumerate important improved models of SSM. We then elaborate on Mamba's selectivity mechanism and hardware-aware state extension， summarising 11 improved models of Mamba in Table 1 and describing the characteristics of each.Second，in the field of medical image processing，the SSM model represented by Mamba has been used for tasks such as image s
egmentation，classification，alignment and fusion，reconstruction，etc.，especially in the task of graphic segmentation，it has achieved excellent results，in addition to breakthroughs in disease prediction，medical image synthesis，radiotherapy dose prediction，and so on.We explore the improvement and application of the SSM model to each of the medical graphics processing tasks.Medical image segmentation is very suitable for the application of SSM model，because the segmentation task corresponds to the long sequence characteristic.Currently，there are nearly forty papers on medical image segmentation based on SSM，and only one model called Vivim performs video segmentation，while the rest of the models perform image segmentation.The vast majority of the models use the U-net architecture，which has achieved remarkable success in various medical image segmentation tasks.The exploration of SSM based medical image segmentation is elaborated and the technical features of each model，the ROI studied，the image modalities involved are analysed one by one and presented in Table 2.Medical image classification is an important task in the field of medical image processing and analysis，but there are not many applications of SSM because the image classification task is not compatible with long sequences or autoregressive properties，so there are only five main models.In medical image registration and fusion，multimodal medical image registration has always been a major challenge in image registration.There are not many researches in medical image registration and fusion based on SSM，and there are only two models，but they break through the challenge of multimodality.There are also two Mamba-based models that have achieved good results in medical image fusion.The task of medical image reconstruction needs to deal with long sequences，which is more suitable for the application of SSM models.At present，researchers have proposed five SSM-based models for medical image reconstruction.One SSM-based model each has been proposed by researchers in the fi
elds of medical image synthesis，disease prediction，radiotherapy dose prediction，and surgical stage identification.We have analysed the technical characteristics of all the models for each of the above tasks，one by one，and described why they achieved good experimental results.Finally，we conclude with a discussion of the challenges facing state-space modelling，which consist of five main areas.The first is the suboptimal performance of the SSM model for visual tasks with non-causal attributes.For data with non-causal attributes，the inherent limitations in the receptive field can be mutually compensated for by using bidirectional scanning and cross-scanning to extend the scanning direction to capture the spatial information inherent in 2D or high-dimensional medical visual data.Despite these adaptations，the task of how to deal with non-causal attributes remains a challenging one.The second is that the scanning scheme needs to be further optimised.Most of the medical image processing tasks are multi-dimensional data，and new scanning schemes need to be designed to enhance the feature learning of SSM，to make full use of the potential of high-dimensional non-causal visual data，and to improve the accuracy of SSM-based models in medical image processing tasks.The third is the need to further improve the generalisation and robustness of the model.Hidden states can accumulate or even amplify domain-specific information，which can，among other things，affect the generalisation performance of the model.The fourth is to improve the efficient fine-tuning of the underlying model.When SSM is developed as an infrastructure，pre-training models on a wide range of datasets such as ImageNet requires research into parameter-efficient fine-tuning techniques.How to perform parameter-efficient fine-tuning in SSM-based models to minimise parameter tuning during the fine-tuning process and to reduce the demand for a large amount of computational resources is a hot research direction with little research at present but with a high demand for app
lications.The fifth is that the adaptability to specific medical image tasks needs to be improved.This is because medical image datasets are usually small and of limited diversity，which makes the model prone to overfitting，and the Mamba model may need to be further optimised in terms of efficient learning using limited data to reduce the reliance on large amounts of annotated data.In addition，the research discussed in this paper and its open source implementation are available on GitHub at
https：//github.com/wyl32123/ssm-medical-paper/tree/main
https://github.com/wyl32123/ssm-medical-paper/tree/main
.
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Yang Z F ， Zhang J J ， Wang G ， Kalra M K and Yan P . 2024f . Cardiovascular Disease Detection from Multi-View Chest X-rays with BI-Mamba ［EB/OL］. ［ 2024-09-19 ］. https://arxiv.org/pdf/2405.18533.pdf https://arxiv.org/pdf/2405.18533.pdf
Ye Z and Chen T X . 2024 . P-mamba： Marrying perona malik diffusion with mamba for efficient pediatric echocardiographic left ventricular segmentation ［EB/OL］. ［ 2024-09-19 ］. https://arxiv.org/pdf/2402.08506.pdf https://arxiv.org/pdf/2402.08506.pdf
Ye Z ， Chen T ， Wang D ， Wang F and Zhang L . 2024 . HFE-Mamba：High-frequency Enhanced Mamba Network for Efficient Segmentation of Left Ventricle in Pediatric Echocardiograms . IEEE Access ， 00 （ 0 ）： 1 - 1 ［ DOI： 10.1109/ACCESS.2024.3424546 http://dx.doi.org/10.1109/ACCESS.2024.3424546 ］
Yuan C Y ， Zhao D F and Agaian S S . 2024 . MUCM-Net： A Mamba Powered UCM-Net for Skin Lesion Segmentation ［EB/OL］. ［ 2024-09-19 ］. https://arxiv.org/pdf/2405.15925.pdf https://arxiv.org/pdf/2405.15925.pdf
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Yu W H and Wang X C . 2024 . MambaOut：Do We Really Need Mamba for Vision？ ［J］. arXiv preprint arXiv ： 2405 . 07992 .［2024-09-19］.
Zhang C ， Cui W R and Guo J J . 2024b . MSSC-BiMamba： Multimodal Sleep Stage Classification and Early Diagnosis of Sleep Disorders with Bidirectional Mamba ［EB/OL］. ［ 2024-09-19 ］. https://arxiv.org/pdf/2405.20142.pdf https://arxiv.org/pdf/2405.20142.pdf
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Zhang H ， Zhao Y H ， Wang L J ， Wang J S ， Zhang W Y ， Chen Y-C and Xiong N . 2024c . Pocket Convolution Mamba for Brain Tumor Segmentation . The Journal of Supercomputing ，，［ DOI： 10.21203/rs.3.rs-4399204/v1 http://dx.doi.org/10.21203/rs.3.rs-4399204/v1 ］
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Zhou R ， Wang J ， Xia G J ， Xing J Y ， Shen H M and Shen X Y . 2024c . Cascade residual multiscale convolution and mamba-structured unet for advanced brain tumor image segmentation . Entropy ， 26 （ 5 ）： 385 ［ DOI： 10.3390/e26050385 http://dx.doi.org/10.3390/e26050385 ］
Zhu L H ， Liao B C ， Zhang Q ， Wang X L ， Liu W Y and Wang X G . 2024 . Vision mamba： Efficient visual representation learning with bidirectional state space model ［EB/OL］. ［ 2024-09-19 ］. https://arxiv.org/pdf/2401.09417.pdf https://arxiv.org/pdf/2401.09417.pdf
Zou B F ， Huang X R ， Jiang Y T ， Jin K and Sun Y Q . 2024a . DeMambaNet： Deformable Convolution and Mamba Integration Network for High-Precision Segmentation of Ambiguously Defined Dental Radicular Boundaries ［EB/OL］. Sensors ， https://www.mdpi.com/1424-8220/24/14/4748 https://www.mdpi.com/1424-8220/24/14/4748
Zou J ， Liu L Q ， Chen Q ， Wang S J ， Xing X H and Qin J . 2024b . MMR-Mamba： Multi-Contrast MRI Reconstruction with Mamba and Spatial-Frequency Information Fusion ［EB/OL］. ［ 2024-09-19 ］. https://arxiv.org/pdf/2406.18950.pdf https://arxiv.org/pdf/2406.18950.pdf
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