Accurate 3D Medical Image Segmentation with Mambas Article Swipe
Luca Lumetti
,
Vittorio Pipoli
,
Kevin Marchesini
,
Elisa Ficarra
,
Costantino Grana
,
Federico Bolelli
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/isbi60581.2025.10981167
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/isbi60581.2025.10981167
CNNs and Transformer-based architectures are recently dominating the field of 3D medical segmentation. While CNNs face limitations in the local receptive field, Transformers require significant memory and data, making them less suitable for analyzing large 3D medical volumes. Consequently, fully convolutional network models like U-Net are still leading the 3D segmentation scenario. Although efforts have been made to reduce the Transformers computational complexity, such optimized models still struggle with content-based reasoning. This paper examines Mamba, a Recurrent Neural Network (RNN) based on State Space Models (SSMs), which achieves linear complexity and has outperformed Transformers in long-sequence tasks. Specifically, we assess Mamba’s performance in 3D medical segmentation using three widely recognized and commonly employed datasets and propose architectural enhancements to improve its segmentation effectiveness by mitigating the primary shortcomings of existing Mamba-based solutions.
Related Topics
Concepts
Computer vision
Artificial intelligence
Computer science
Image segmentation
Segmentation
Scale-space segmentation
Image (mathematics)
Metadata
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- article
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- en
- Landing Page
- https://doi.org/10.1109/isbi60581.2025.10981167
- OA Status
- green
- Cited By
- 1
- References
- 32
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- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.1109/isbi60581.2025.10981167Digital Object Identifier
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Accurate 3D Medical Image Segmentation with MambasWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
- Publication date
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2025-04-14Full publication date if available
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Luca Lumetti, Vittorio Pipoli, Kevin Marchesini, Elisa Ficarra, Costantino Grana, Federico BolelliList of authors in order
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https://doi.org/10.1109/isbi60581.2025.10981167Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://hdl.handle.net/11380/1367190Direct OA link when available
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Computer vision, Artificial intelligence, Computer science, Image segmentation, Segmentation, Scale-space segmentation, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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| abstract_inverted_index.tasks. | 96 |
| abstract_inverted_index.widely | 108 |
| abstract_inverted_index.(SSMs), | 85 |
| abstract_inverted_index.Network | 78 |
| abstract_inverted_index.efforts | 53 |
| abstract_inverted_index.improve | 119 |
| abstract_inverted_index.leading | 47 |
| abstract_inverted_index.medical | 11, 36, 104 |
| abstract_inverted_index.network | 41 |
| abstract_inverted_index.primary | 126 |
| abstract_inverted_index.propose | 115 |
| abstract_inverted_index.require | 23 |
| abstract_inverted_index.Although | 52 |
| abstract_inverted_index.achieves | 87 |
| abstract_inverted_index.commonly | 111 |
| abstract_inverted_index.datasets | 113 |
| abstract_inverted_index.employed | 112 |
| abstract_inverted_index.examines | 73 |
| abstract_inverted_index.existing | 129 |
| abstract_inverted_index.recently | 5 |
| abstract_inverted_index.struggle | 67 |
| abstract_inverted_index.suitable | 31 |
| abstract_inverted_index.volumes. | 37 |
| abstract_inverted_index.Mamba’s | 100 |
| abstract_inverted_index.Recurrent | 76 |
| abstract_inverted_index.analyzing | 33 |
| abstract_inverted_index.optimized | 64 |
| abstract_inverted_index.receptive | 20 |
| abstract_inverted_index.scenario. | 51 |
| abstract_inverted_index.complexity | 89 |
| abstract_inverted_index.dominating | 6 |
| abstract_inverted_index.mitigating | 124 |
| abstract_inverted_index.reasoning. | 70 |
| abstract_inverted_index.recognized | 109 |
| abstract_inverted_index.solutions. | 131 |
| abstract_inverted_index.Mamba-based | 130 |
| abstract_inverted_index.complexity, | 62 |
| abstract_inverted_index.limitations | 16 |
| abstract_inverted_index.performance | 101 |
| abstract_inverted_index.significant | 24 |
| abstract_inverted_index.Transformers | 22, 60, 93 |
| abstract_inverted_index.enhancements | 117 |
| abstract_inverted_index.outperformed | 92 |
| abstract_inverted_index.segmentation | 50, 105, 121 |
| abstract_inverted_index.shortcomings | 127 |
| abstract_inverted_index.Consequently, | 38 |
| abstract_inverted_index.Specifically, | 97 |
| abstract_inverted_index.architectural | 116 |
| abstract_inverted_index.architectures | 3 |
| abstract_inverted_index.computational | 61 |
| abstract_inverted_index.content-based | 69 |
| abstract_inverted_index.convolutional | 40 |
| abstract_inverted_index.effectiveness | 122 |
| abstract_inverted_index.long-sequence | 95 |
| abstract_inverted_index.segmentation. | 12 |
| abstract_inverted_index.Transformer-based | 2 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.8711267 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |