Cine cardiac magnetic resonance segmentation using temporal-spatial adaptation of prompt-enabled segment-anything-model: a feasibility study
ABSTRACT: Background: We propose an approach to adapt a segmentation foundation model, segment-anything-model (SAM), for cine cardiovascular magnetic resonance (CMR) segmentation and evaluate its generalization performance on unseen datasets. Methods: We present our model, cineCMR-SAM, which introd...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Journal of Cardiovascular Magnetic Resonance |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1097664725000717 |
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| author | Zhennong Chen Sekeun Kim Hui Ren Sunghwan Kim Siyeop Yoon Quanzheng Li Xiang Li |
| author_facet | Zhennong Chen Sekeun Kim Hui Ren Sunghwan Kim Siyeop Yoon Quanzheng Li Xiang Li |
| author_sort | Zhennong Chen |
| collection | DOAJ |
| description | ABSTRACT: Background: We propose an approach to adapt a segmentation foundation model, segment-anything-model (SAM), for cine cardiovascular magnetic resonance (CMR) segmentation and evaluate its generalization performance on unseen datasets. Methods: We present our model, cineCMR-SAM, which introduces a temporal-spatial attention mechanism to produce segmentation across one cardiac cycle. We freeze the pre-trained SAM’s weights to leverage SAM’s generalizability while fine-tuning the rest of the model on two public cine CMR datasets. Our model also enables text prompts to specify the view type (short-axis or long-axis) of the input slices and box prompts to guide the segmentation region. We evaluated our model’s generalization performance on three external testing datasets including a public multi-center, multi-vendor testing dataset of 136 cases and 2 retrospectively collected in-house datasets from 2 different centers with specific pathologies: aortic stenosis (40 cases) and heart failure with preserved ejection fraction (HFpEF) (53 cases). Results: Our approach achieved superior generalization in both the public testing dataset (Dice for LV=0.94 and for myocardium=0.86) and two in-house datasets (Dice ≥0.90 for LV and ≥0.82 for myocardium) compared to existing CMR deep learning segmentation methods. Clinical parameters derived from automatic and manual segmentations showed a strong correlation (r ≥0.90). The use of both text prompts and box prompts enhanced the segmentation accuracy. Conclusion: cineCMR-SAM effectively adapts SAM for cine CMR segmentation, achieving high generalizability and superior accuracy on unseen datasets. |
| format | Article |
| id | doaj-art-cb532442e05a44839e09bdcf7c4b437e |
| institution | OA Journals |
| issn | 1097-6647 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Cardiovascular Magnetic Resonance |
| spelling | doaj-art-cb532442e05a44839e09bdcf7c4b437e2025-08-20T02:34:16ZengElsevierJournal of Cardiovascular Magnetic Resonance1097-66472025-01-0127110190910.1016/j.jocmr.2025.101909Cine cardiac magnetic resonance segmentation using temporal-spatial adaptation of prompt-enabled segment-anything-model: a feasibility studyZhennong Chen0Sekeun Kim1Hui Ren2Sunghwan Kim3Siyeop Yoon4Quanzheng Li5Xiang Li6From Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USAFrom Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USAFrom Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USAFrom Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USAFrom Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USAFrom Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USACorresponding author.; From Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USAABSTRACT: Background: We propose an approach to adapt a segmentation foundation model, segment-anything-model (SAM), for cine cardiovascular magnetic resonance (CMR) segmentation and evaluate its generalization performance on unseen datasets. Methods: We present our model, cineCMR-SAM, which introduces a temporal-spatial attention mechanism to produce segmentation across one cardiac cycle. We freeze the pre-trained SAM’s weights to leverage SAM’s generalizability while fine-tuning the rest of the model on two public cine CMR datasets. Our model also enables text prompts to specify the view type (short-axis or long-axis) of the input slices and box prompts to guide the segmentation region. We evaluated our model’s generalization performance on three external testing datasets including a public multi-center, multi-vendor testing dataset of 136 cases and 2 retrospectively collected in-house datasets from 2 different centers with specific pathologies: aortic stenosis (40 cases) and heart failure with preserved ejection fraction (HFpEF) (53 cases). Results: Our approach achieved superior generalization in both the public testing dataset (Dice for LV=0.94 and for myocardium=0.86) and two in-house datasets (Dice ≥0.90 for LV and ≥0.82 for myocardium) compared to existing CMR deep learning segmentation methods. Clinical parameters derived from automatic and manual segmentations showed a strong correlation (r ≥0.90). The use of both text prompts and box prompts enhanced the segmentation accuracy. Conclusion: cineCMR-SAM effectively adapts SAM for cine CMR segmentation, achieving high generalizability and superior accuracy on unseen datasets.http://www.sciencedirect.com/science/article/pii/S1097664725000717CMRSegmentationDeep learningSegment-anything-model |
| spellingShingle | Zhennong Chen Sekeun Kim Hui Ren Sunghwan Kim Siyeop Yoon Quanzheng Li Xiang Li Cine cardiac magnetic resonance segmentation using temporal-spatial adaptation of prompt-enabled segment-anything-model: a feasibility study Journal of Cardiovascular Magnetic Resonance CMR Segmentation Deep learning Segment-anything-model |
| title | Cine cardiac magnetic resonance segmentation using temporal-spatial adaptation of prompt-enabled segment-anything-model: a feasibility study |
| title_full | Cine cardiac magnetic resonance segmentation using temporal-spatial adaptation of prompt-enabled segment-anything-model: a feasibility study |
| title_fullStr | Cine cardiac magnetic resonance segmentation using temporal-spatial adaptation of prompt-enabled segment-anything-model: a feasibility study |
| title_full_unstemmed | Cine cardiac magnetic resonance segmentation using temporal-spatial adaptation of prompt-enabled segment-anything-model: a feasibility study |
| title_short | Cine cardiac magnetic resonance segmentation using temporal-spatial adaptation of prompt-enabled segment-anything-model: a feasibility study |
| title_sort | cine cardiac magnetic resonance segmentation using temporal spatial adaptation of prompt enabled segment anything model a feasibility study |
| topic | CMR Segmentation Deep learning Segment-anything-model |
| url | http://www.sciencedirect.com/science/article/pii/S1097664725000717 |
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