Taming Mambas for 3D Medical Image Segmentation
Recently, the field of 3D medical segmentation has been dominated by deep learning models employing Convolutional Neural Networks (CNNs) and Transformer-based architectures, each with its distinctive strengths and limitations. CNNs are constrained by a local receptive field, whereas Transformers are...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11005448/ |
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| author | Luca Lumetti Vittorio Pipoli Kevin Marchesini Elisa Ficarra Costantino Grana Federico Bolelli |
| author_facet | Luca Lumetti Vittorio Pipoli Kevin Marchesini Elisa Ficarra Costantino Grana Federico Bolelli |
| author_sort | Luca Lumetti |
| collection | DOAJ |
| description | Recently, the field of 3D medical segmentation has been dominated by deep learning models employing Convolutional Neural Networks (CNNs) and Transformer-based architectures, each with its distinctive strengths and limitations. CNNs are constrained by a local receptive field, whereas Transformers are hindered by their substantial memory requirements as well as their data hunger, making them not ideal for processing 3D medical volumes at a fine-grained level. For these reasons, fully convolutional neural networks, as nnU-Net, still dominate the scene when segmenting medical structures in large 3D medical volumes. Despite numerous advancements toward developing Transformer variants with subquadratic time and memory complexity, these models still fall short in content-based reasoning. A recent breakthrough is Mamba, a Recurrent Neural Network (RNN) based on State-Space Models (SSMs), outperforming Transformers in many long-context tasks (million-length sequences) on famous natural language processing and genomic benchmarks while keeping a linear complexity. In this paper, we evaluate the effectiveness of Mamba-based architectures in comparison to state-of-the-art convolutional and Transformer-based models for 3D medical image segmentation across three well-established datasets: Synapse Abdomen, MSD BrainTumor, and ACDC. Additionally, we address the primary limitations of existing Mamba-based architectures by proposing alternative architectural designs, hence improving segmentation performances. The source code is publicly available to ensure reproducibility and facilitate further research: <uri>https://github.com/LucaLumetti/TamingMambas</uri> |
| format | Article |
| id | doaj-art-9bb77c0f9a50407b92e98af6735a5ad6 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9bb77c0f9a50407b92e98af6735a5ad62025-08-20T02:34:36ZengIEEEIEEE Access2169-35362025-01-0113897488975910.1109/ACCESS.2025.357046111005448Taming Mambas for 3D Medical Image SegmentationLuca Lumetti0https://orcid.org/0000-0002-1046-332XVittorio Pipoli1https://orcid.org/0009-0008-5749-6007Kevin Marchesini2Elisa Ficarra3Costantino Grana4https://orcid.org/0000-0002-4792-2358Federico Bolelli5https://orcid.org/0000-0002-5299-6351Department of Engineering “Enzo Ferrari,”, University of Modena and Reggio Emilia, Modena, ItalyDepartment of Engineering “Enzo Ferrari,”, University of Modena and Reggio Emilia, Modena, ItalyDepartment of Engineering “Enzo Ferrari,”, University of Modena and Reggio Emilia, Modena, ItalyDepartment of Engineering “Enzo Ferrari,”, University of Modena and Reggio Emilia, Modena, ItalyDepartment of Engineering “Enzo Ferrari,”, University of Modena and Reggio Emilia, Modena, ItalyDepartment of Engineering “Enzo Ferrari,”, University of Modena and Reggio Emilia, Modena, ItalyRecently, the field of 3D medical segmentation has been dominated by deep learning models employing Convolutional Neural Networks (CNNs) and Transformer-based architectures, each with its distinctive strengths and limitations. CNNs are constrained by a local receptive field, whereas Transformers are hindered by their substantial memory requirements as well as their data hunger, making them not ideal for processing 3D medical volumes at a fine-grained level. For these reasons, fully convolutional neural networks, as nnU-Net, still dominate the scene when segmenting medical structures in large 3D medical volumes. Despite numerous advancements toward developing Transformer variants with subquadratic time and memory complexity, these models still fall short in content-based reasoning. A recent breakthrough is Mamba, a Recurrent Neural Network (RNN) based on State-Space Models (SSMs), outperforming Transformers in many long-context tasks (million-length sequences) on famous natural language processing and genomic benchmarks while keeping a linear complexity. In this paper, we evaluate the effectiveness of Mamba-based architectures in comparison to state-of-the-art convolutional and Transformer-based models for 3D medical image segmentation across three well-established datasets: Synapse Abdomen, MSD BrainTumor, and ACDC. Additionally, we address the primary limitations of existing Mamba-based architectures by proposing alternative architectural designs, hence improving segmentation performances. The source code is publicly available to ensure reproducibility and facilitate further research: <uri>https://github.com/LucaLumetti/TamingMambas</uri>https://ieeexplore.ieee.org/document/11005448/Medical imaging3D segmentationMambaU-NettransformersRNNs |
| spellingShingle | Luca Lumetti Vittorio Pipoli Kevin Marchesini Elisa Ficarra Costantino Grana Federico Bolelli Taming Mambas for 3D Medical Image Segmentation IEEE Access Medical imaging 3D segmentation Mamba U-Net transformers RNNs |
| title | Taming Mambas for 3D Medical Image Segmentation |
| title_full | Taming Mambas for 3D Medical Image Segmentation |
| title_fullStr | Taming Mambas for 3D Medical Image Segmentation |
| title_full_unstemmed | Taming Mambas for 3D Medical Image Segmentation |
| title_short | Taming Mambas for 3D Medical Image Segmentation |
| title_sort | taming mambas for 3d medical image segmentation |
| topic | Medical imaging 3D segmentation Mamba U-Net transformers RNNs |
| url | https://ieeexplore.ieee.org/document/11005448/ |
| work_keys_str_mv | AT lucalumetti tamingmambasfor3dmedicalimagesegmentation AT vittoriopipoli tamingmambasfor3dmedicalimagesegmentation AT kevinmarchesini tamingmambasfor3dmedicalimagesegmentation AT elisaficarra tamingmambasfor3dmedicalimagesegmentation AT costantinograna tamingmambasfor3dmedicalimagesegmentation AT federicobolelli tamingmambasfor3dmedicalimagesegmentation |