Efficient few-shot medical image segmentation via self-supervised variational autoencoder
Few-shot medical image segmentation typically uses a joint model for registration and segmentation. The registration model aligns a labeled atlas with unlabeled images to form initial masks, which are then refined by the segmentation model. However, inevitable spatial misalignments during registrati...
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| Vydáno v: | Medical image analysis Ročník 104; s. 103637 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.08.2025
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| ISSN: | 1361-8415, 1361-8423, 1361-8423 |
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| Abstract | Few-shot medical image segmentation typically uses a joint model for registration and segmentation. The registration model aligns a labeled atlas with unlabeled images to form initial masks, which are then refined by the segmentation model. However, inevitable spatial misalignments during registration can lead to inaccuracies and diminished segmentation quality. To address this, we developed EFS-MedSeg, an end-to-end model using two labeled atlases and few unlabeled images, enhanced by data augmentation and self-supervised learning. Initially, EFS-MedSeg applies a 3D random regional switch strategy to augment atlases, thereby enhancing supervision in segmentation tasks. This not only introduces variability to the training data but also enhances the model’s ability to generalize and prevents overfitting, resulting in natural and smooth label boundaries. Following this, we use a variational autoencoder for a weighted reconstruction task, focusing the model’s attention on areas with lower Dice scores to ensure accurate segmentation that conforms to the atlas image’s shape and structural appearance. Moreover, we introduce a self-contrastive module aimed at improving feature extraction, guided by anatomical structure priors, thus enhancing the model’s convergence and segmentation accuracy. Results on multi-modal medical image datasets show that EFS-MedSeg achieves performance comparable to fully-supervised methods. Moreover, it consistently surpasses the second-best method in Dice score by 1.4%, 9.1%, and 1.1% on the OASIS, BCV, and BCH datasets, respectively, highlighting its robustness and adaptability across diverse datasets. The source code will be made publicly available at: https://github.com/NoviceFodder/EFS-MedSeg.
•Registration-free model enhances few-shot segmentation with 3D random regional switch.•Self-supervised framework refines low-Dice areas for precise segmentation.•Adaptive attention mechanism balances tissue volume discrepancies and boosts accuracy.•Self-contrastive module leverages anatomical priors for better features and outcomes. |
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| AbstractList | Few-shot medical image segmentation typically uses a joint model for registration and segmentation. The registration model aligns a labeled atlas with unlabeled images to form initial masks, which are then refined by the segmentation model. However, inevitable spatial misalignments during registration can lead to inaccuracies and diminished segmentation quality. To address this, we developed EFS-MedSeg, an end-to-end model using two labeled atlases and few unlabeled images, enhanced by data augmentation and self-supervised learning. Initially, EFS-MedSeg applies a 3D random regional switch strategy to augment atlases, thereby enhancing supervision in segmentation tasks. This not only introduces variability to the training data but also enhances the model's ability to generalize and prevents overfitting, resulting in natural and smooth label boundaries. Following this, we use a variational autoencoder for a weighted reconstruction task, focusing the model's attention on areas with lower Dice scores to ensure accurate segmentation that conforms to the atlas image's shape and structural appearance. Moreover, we introduce a self-contrastive module aimed at improving feature extraction, guided by anatomical structure priors, thus enhancing the model's convergence and segmentation accuracy. Results on multi-modal medical image datasets show that EFS-MedSeg achieves performance comparable to fully-supervised methods. Moreover, it consistently surpasses the second-best method in Dice score by 1.4%, 9.1%, and 1.1% on the OASIS, BCV, and BCH datasets, respectively, highlighting its robustness and adaptability across diverse datasets. The source code will be made publicly available at: https://github.com/NoviceFodder/EFS-MedSeg.Few-shot medical image segmentation typically uses a joint model for registration and segmentation. The registration model aligns a labeled atlas with unlabeled images to form initial masks, which are then refined by the segmentation model. However, inevitable spatial misalignments during registration can lead to inaccuracies and diminished segmentation quality. To address this, we developed EFS-MedSeg, an end-to-end model using two labeled atlases and few unlabeled images, enhanced by data augmentation and self-supervised learning. Initially, EFS-MedSeg applies a 3D random regional switch strategy to augment atlases, thereby enhancing supervision in segmentation tasks. This not only introduces variability to the training data but also enhances the model's ability to generalize and prevents overfitting, resulting in natural and smooth label boundaries. Following this, we use a variational autoencoder for a weighted reconstruction task, focusing the model's attention on areas with lower Dice scores to ensure accurate segmentation that conforms to the atlas image's shape and structural appearance. Moreover, we introduce a self-contrastive module aimed at improving feature extraction, guided by anatomical structure priors, thus enhancing the model's convergence and segmentation accuracy. Results on multi-modal medical image datasets show that EFS-MedSeg achieves performance comparable to fully-supervised methods. Moreover, it consistently surpasses the second-best method in Dice score by 1.4%, 9.1%, and 1.1% on the OASIS, BCV, and BCH datasets, respectively, highlighting its robustness and adaptability across diverse datasets. The source code will be made publicly available at: https://github.com/NoviceFodder/EFS-MedSeg. Few-shot medical image segmentation typically uses a joint model for registration and segmentation. The registration model aligns a labeled atlas with unlabeled images to form initial masks, which are then refined by the segmentation model. However, inevitable spatial misalignments during registration can lead to inaccuracies and diminished segmentation quality. To address this, we developed EFS-MedSeg, an end-to-end model using two labeled atlases and few unlabeled images, enhanced by data augmentation and self-supervised learning. Initially, EFS-MedSeg applies a 3D random regional switch strategy to augment atlases, thereby enhancing supervision in segmentation tasks. This not only introduces variability to the training data but also enhances the model’s ability to generalize and prevents overfitting, resulting in natural and smooth label boundaries. Following this, we use a variational autoencoder for a weighted reconstruction task, focusing the model’s attention on areas with lower Dice scores to ensure accurate segmentation that conforms to the atlas image’s shape and structural appearance. Moreover, we introduce a self-contrastive module aimed at improving feature extraction, guided by anatomical structure priors, thus enhancing the model’s convergence and segmentation accuracy. Results on multi-modal medical image datasets show that EFS-MedSeg achieves performance comparable to fully-supervised methods. Moreover, it consistently surpasses the second-best method in Dice score by 1.4%, 9.1%, and 1.1% on the OASIS, BCV, and BCH datasets, respectively, highlighting its robustness and adaptability across diverse datasets. The source code will be made publicly available at: https://github.com/NoviceFodder/EFS-MedSeg. •Registration-free model enhances few-shot segmentation with 3D random regional switch.•Self-supervised framework refines low-Dice areas for precise segmentation.•Adaptive attention mechanism balances tissue volume discrepancies and boosts accuracy.•Self-contrastive module leverages anatomical priors for better features and outcomes. Few-shot medical image segmentation typically uses a joint model for registration and segmentation. The registration model aligns a labeled atlas with unlabeled images to form initial masks, which are then refined by the segmentation model. However, inevitable spatial misalignments during registration can lead to inaccuracies and diminished segmentation quality. To address this, we developed EFS-MedSeg, an end-to-end model using two labeled atlases and few unlabeled images, enhanced by data augmentation and self-supervised learning. Initially, EFS-MedSeg applies a 3D random regional switch strategy to augment atlases, thereby enhancing supervision in segmentation tasks. This not only introduces variability to the training data but also enhances the model's ability to generalize and prevents overfitting, resulting in natural and smooth label boundaries. Following this, we use a variational autoencoder for a weighted reconstruction task, focusing the model's attention on areas with lower Dice scores to ensure accurate segmentation that conforms to the atlas image's shape and structural appearance. Moreover, we introduce a self-contrastive module aimed at improving feature extraction, guided by anatomical structure priors, thus enhancing the model's convergence and segmentation accuracy. Results on multi-modal medical image datasets show that EFS-MedSeg achieves performance comparable to fully-supervised methods. Moreover, it consistently surpasses the second-best method in Dice score by 1.4%, 9.1%, and 1.1% on the OASIS, BCV, and BCH datasets, respectively, highlighting its robustness and adaptability across diverse datasets. The source code will be made publicly available at: https://github.com/NoviceFodder/EFS-MedSeg. |
| ArticleNumber | 103637 |
| Author | Carlson, David E. Zhou, Feng Peng, Yun Xi, Fengjun Zhou, Yanjie Tu, Liyun Liu, Yong |
| Author_xml | – sequence: 1 givenname: Yanjie surname: Zhou fullname: Zhou, Yanjie organization: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China – sequence: 2 givenname: Feng surname: Zhou fullname: Zhou, Feng organization: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China – sequence: 3 givenname: Fengjun surname: Xi fullname: Xi, Fengjun organization: Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China – sequence: 4 givenname: Yong surname: Liu fullname: Liu, Yong organization: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China – sequence: 5 givenname: Yun surname: Peng fullname: Peng, Yun email: ppengyun@hotmail.com organization: Department of Radiology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China – sequence: 6 givenname: David E. orcidid: 0000-0003-1005-6385 surname: Carlson fullname: Carlson, David E. organization: Department of Civil and Environmental Engineering, and the Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA – sequence: 7 givenname: Liyun orcidid: 0000-0002-3389-400X surname: Tu fullname: Tu, Liyun email: tuliyun@bupt.edu.cn organization: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China |
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| Keywords | Variational autoencoder Few-shot learning Image reconstruction Medical image segmentation |
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| Snippet | Few-shot medical image segmentation typically uses a joint model for registration and segmentation. The registration model aligns a labeled atlas with... |
| SourceID | proquest pubmed crossref elsevier |
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| SubjectTerms | Algorithms Autoencoder Few-shot learning Humans Image Interpretation, Computer-Assisted - methods Image Processing, Computer-Assisted - methods Image reconstruction Magnetic Resonance Imaging Medical image segmentation Supervised Machine Learning Variational autoencoder |
| Title | Efficient few-shot medical image segmentation via self-supervised variational autoencoder |
| URI | https://dx.doi.org/10.1016/j.media.2025.103637 https://www.ncbi.nlm.nih.gov/pubmed/40449308 https://www.proquest.com/docview/3214326016 |
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