A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
Accurate medical image segmentation plays a critical role in clinical diagnosis, treatment planning, and a wide range of healthcare applications. Although U-shaped CNNs and Transformer-based architectures have shown promise, CNNs struggle to capture long-range dependencies, whereas Transformers suff...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/14/7821 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849409138258345984 |
|---|---|
| author | Xiaoxuan Ma Yingao Du Dong Sui |
| author_facet | Xiaoxuan Ma Yingao Du Dong Sui |
| author_sort | Xiaoxuan Ma |
| collection | DOAJ |
| description | Accurate medical image segmentation plays a critical role in clinical diagnosis, treatment planning, and a wide range of healthcare applications. Although U-shaped CNNs and Transformer-based architectures have shown promise, CNNs struggle to capture long-range dependencies, whereas Transformers suffer from quadratic growth in computational cost as image resolution increases. To address these issues, we propose HCMUNet, a novel medical image segmentation model that innovatively combines the local feature extraction capabilities of CNNs with the efficient long-range dependency modeling of Mamba, enhancing feature representation while reducing computational cost. In addition, HCMUNet features a redesigned skip connection and a novel attention module that integrates multi-scale features to recover spatial details lost during down-sampling and to promote richer cross-dimensional interactions. HCMUNet achieves Dice Similarity Coefficients (DSC) of 90.32%, 81.52%, and 92.11% on the ISIC 2018, Synapse multi-organ, and ACDC datasets, respectively, outperforming baseline methods by 0.65%, 1.05%, and 1.39%. Furthermore, HCMUNet consistently outperforms U-Net and Swin-UNet, achieving average Dice score improvements of approximately 5% and 2% across the evaluated datasets. These results collectively affirm the effectiveness and reliability of the proposed model across different segmentation tasks. |
| format | Article |
| id | doaj-art-50e76f080c954652b5c95044e350077e |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-50e76f080c954652b5c95044e350077e2025-08-20T03:35:36ZengMDPI AGApplied Sciences2076-34172025-07-011514782110.3390/app15147821A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image SegmentationXiaoxuan Ma0Yingao Du1Dong Sui2School of Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaAccurate medical image segmentation plays a critical role in clinical diagnosis, treatment planning, and a wide range of healthcare applications. Although U-shaped CNNs and Transformer-based architectures have shown promise, CNNs struggle to capture long-range dependencies, whereas Transformers suffer from quadratic growth in computational cost as image resolution increases. To address these issues, we propose HCMUNet, a novel medical image segmentation model that innovatively combines the local feature extraction capabilities of CNNs with the efficient long-range dependency modeling of Mamba, enhancing feature representation while reducing computational cost. In addition, HCMUNet features a redesigned skip connection and a novel attention module that integrates multi-scale features to recover spatial details lost during down-sampling and to promote richer cross-dimensional interactions. HCMUNet achieves Dice Similarity Coefficients (DSC) of 90.32%, 81.52%, and 92.11% on the ISIC 2018, Synapse multi-organ, and ACDC datasets, respectively, outperforming baseline methods by 0.65%, 1.05%, and 1.39%. Furthermore, HCMUNet consistently outperforms U-Net and Swin-UNet, achieving average Dice score improvements of approximately 5% and 2% across the evaluated datasets. These results collectively affirm the effectiveness and reliability of the proposed model across different segmentation tasks.https://www.mdpi.com/2076-3417/15/14/7821medical image segmentationMambaConvolutional Neural Networkshybrid architecturesState Space Model |
| spellingShingle | Xiaoxuan Ma Yingao Du Dong Sui A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation Applied Sciences medical image segmentation Mamba Convolutional Neural Networks hybrid architectures State Space Model |
| title | A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation |
| title_full | A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation |
| title_fullStr | A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation |
| title_full_unstemmed | A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation |
| title_short | A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation |
| title_sort | u shaped architecture based on hybrid cnn and mamba for medical image segmentation |
| topic | medical image segmentation Mamba Convolutional Neural Networks hybrid architectures State Space Model |
| url | https://www.mdpi.com/2076-3417/15/14/7821 |
| work_keys_str_mv | AT xiaoxuanma aushapedarchitecturebasedonhybridcnnandmambaformedicalimagesegmentation AT yingaodu aushapedarchitecturebasedonhybridcnnandmambaformedicalimagesegmentation AT dongsui aushapedarchitecturebasedonhybridcnnandmambaformedicalimagesegmentation AT xiaoxuanma ushapedarchitecturebasedonhybridcnnandmambaformedicalimagesegmentation AT yingaodu ushapedarchitecturebasedonhybridcnnandmambaformedicalimagesegmentation AT dongsui ushapedarchitecturebasedonhybridcnnandmambaformedicalimagesegmentation |