- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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An efficient segment anything model for the segmentation of medical images ; volume:14 ; number:1 ; day:21 ; month:8 ; year:2024 ; pages:1-16 ; date:12.2024
Scientific reports ; 14, Heft 1 (21.8.2024), 1-16, 12.2024
- Creator
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Dong, Guanliang
Wang, Zhangquan
Chen, Yourong
Sun, Yuliang
Song, Hongbo
Liu, Liyuan
Cui, Haidong
- Contributor
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SpringerLink (Online service)
- DOI
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10.1038/s41598-024-70288-8
- URN
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urn:nbn:de:101:1-2411072111537.585039556837
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:21 AM CEST
Data provider
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Associated
- Dong, Guanliang
- Wang, Zhangquan
- Chen, Yourong
- Sun, Yuliang
- Song, Hongbo
- Liu, Liyuan
- Cui, Haidong
- SpringerLink (Online service)
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Segment anything in medical images
QUALITY ASSESSMENT FOR MULTI-RESOLUTION SEGMENTATION AND SEGMENT-ANYTHING MODEL USING WORLDVIEW-3 IMAGERY
Segment Anything in Optical Coherence Tomography: SAM 2 for Volumetric Segmentation of Retinal Biomarkers
Detecting Anything Overlooked in Semantic Segmentation
Search anything: segmentation-based similarity search via region prompts
Automatic segmentation and classification of seven-segment display digits on auroral images
Fast segmentation of anterior segment optical coherence tomography images using graph cut
The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning
An efficient inflation method for segmentation of medical 3D images
Reducing training data using pre-trained foundation models: a case study on traffic sign segmentation using the segment anything model
Evaluating segment anything model (SAM) on MRI scans of brain tumors
Maximizing Information from User-Clicks for Efficient Instance Segmentation in Images
Segment anything in medical images
QUALITY ASSESSMENT FOR MULTI-RESOLUTION SEGMENTATION AND SEGMENT-ANYTHING MODEL USING WORLDVIEW-3 IMAGERY
Segment Anything in Optical Coherence Tomography: SAM 2 for Volumetric Segmentation of Retinal Biomarkers
Detecting Anything Overlooked in Semantic Segmentation
Search anything: segmentation-based similarity search via region prompts
Automatic segmentation and classification of seven-segment display digits on auroral images
Fast segmentation of anterior segment optical coherence tomography images using graph cut
The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning
An efficient inflation method for segmentation of medical 3D images
Reducing training data using pre-trained foundation models: a case study on traffic sign segmentation using the segment anything model
Evaluating segment anything model (SAM) on MRI scans of brain tumors
Maximizing Information from User-Clicks for Efficient Instance Segmentation in Images
Segment anything in medical images
QUALITY ASSESSMENT FOR MULTI-RESOLUTION SEGMENTATION AND SEGMENT-ANYTHING MODEL USING WORLDVIEW-3 IMAGERY
Segment Anything in Optical Coherence Tomography: SAM 2 for Volumetric Segmentation of Retinal Biomarkers
Detecting Anything Overlooked in Semantic Segmentation
Search anything: segmentation-based similarity search via region prompts
Automatic segmentation and classification of seven-segment display digits on auroral images
Fast segmentation of anterior segment optical coherence tomography images using graph cut
The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning
An efficient inflation method for segmentation of medical 3D images
Reducing training data using pre-trained foundation models: a case study on traffic sign segmentation using the segment anything model
Evaluating segment anything model (SAM) on MRI scans of brain tumors