CSSNet: Cascaded spatial shift network for multi-organ segmentation Article Swipe
Yeqin Shao
,
Kunyang Zhou
,
Lichi Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.compbiomed.2024.107955
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.compbiomed.2024.107955
Multi-organ segmentation is vital for clinical diagnosis and treatment. Although CNN and its extensions are popular in organ segmentation, they suffer from the local receptive field. In contrast, MultiLayer-Perceptron-based models (e.g., MLP-Mixer) have a global receptive field. However, these MLP-based models employ fully connected layers with many parameters and tend to overfit on sample-deficient medical image datasets. Therefore, we propose a Cascaded Spatial Shift Network, CSSNet, for multi-organ segmentation. Specifically, we design a novel cascaded spatial shift block to reduce the number of model parameters and aggregate feature segments in a cascaded way for efficient and effective feature extraction. Then, we propose a feature refinement network to aggregate multi-scale features with location information, and enhance the multi-scale features along the channel and spatial axis to obtain a high-quality feature map. Finally, we employ a self-attention-based fusion strategy to focus on the discriminative feature information for better multi-organ segmentation performance. Experimental results on the Synapse (multiply organs) and LiTS (liver & tumor) datasets demonstrate that our CSSNet achieves promising segmentation performance compared with CNN, MLP, and Transformer models. The source code will be available at https://github.com/zkyseu/CSSNet.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.compbiomed.2024.107955
- OA Status
- hybrid
- Cited By
- 13
- References
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4390618662Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.compbiomed.2024.107955Digital Object Identifier
- Title
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CSSNet: Cascaded spatial shift network for multi-organ segmentationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-05Full publication date if available
- Authors
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Yeqin Shao, Kunyang Zhou, Lichi ZhangList of authors in order
- Landing page
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https://doi.org/10.1016/j.compbiomed.2024.107955Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.compbiomed.2024.107955Direct OA link when available
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Computer science, Artificial intelligence, Segmentation, Discriminative model, Pattern recognition (psychology), Overfitting, Feature (linguistics), Image segmentation, Feature extraction, Artificial neural network, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
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2025: 9, 2024: 4Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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