Reducing manual workload in CT and MRI annotation with the Segment Anything Model 2
BMC Medical Imaging, vol. 26, pp. 54
Abstract
BACKGROUND: Volumetric segmentation in CT and MRI is valuable for artificial intelligence workflows in radiology, yet creating the large, precisely annotated datasets required for training segmentation models remains laborious. METHODS: Here, we tested in simulation whether the foundation model “Segment Anything Model 2” (SAM 2) can reduce expert annotation workload. In our workflow, annotators provide a single box at the object’s center, and SAM 2 automatically segments the object across slices; annotators then review and correct the masks as needed. Workload reduction was defined as the proportion of SAM 2’s predicted segmentation masks that were accepted without modification. Downstream segmentation models were then trained on the SAM 2-assisted masks and compared with reference models trained on ground truth masks. RESULTS: For femoral bone segmentation in MRI and liver tumor segmentation in CT, 36,614 sagittal and 16,311 axial slices were annotated, with 30% and 53% of SAM 2-generated masks accepted as is, respectively, indicating workload reduction. Crucially, segmentation models trained on SAM 2-assisted masks performed comparably to reference models, with a median dice similarity coefficient of 98.5% compared with 98.7% for femoral bone segmentation, and 77.3% compared with 77.0% for liver tumor segmentation. CONCLUSION: Using SAM 2 could thus expedite 3D medical imaging dataset annotation and segmentation model development for both research and clinical applications.
Authors 9
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University Hospital Carl Gustav Carus · Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
Institute and Polyclinic for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus Dresden, Technical University Dresden, Dresden, Germany
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Affiliation as printed
Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
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Else Kröner Fresenius Center for Digital Health
Affiliation as printed
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
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TUM Klinikum · Deutsches Herzzentrum München · Technical University of Munich
Affiliation as printed
Department of Cardiovascular Radiology and Nuclear Medicine, Technical University of Munich, School of Medicine and Health, German Heart Center, TUM University Hospital, Munich, Germany
Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, Technical University of Munich, School of Medicine and Health, TUM University Hospital, Munich, Germany
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Else Kröner Fresenius Center for Digital Health
Affiliation as printed
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
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University Hospital Carl Gustav Carus · Technische Universität Dresden
Affiliation as printed
Institute and Polyclinic for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus Dresden, Technical University Dresden, Dresden, Germany
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University Hospital Carl Gustav Carus · Technische Universität Dresden
Affiliation as printed
Institute and Polyclinic for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus Dresden, Technical University Dresden, Dresden, Germany
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Affiliation as printed
Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
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Jakob Nikolas Kather corresponding
University of Leeds · University Hospital Heidelberg · National Center for Tumor Diseases · University Hospital Carl Gustav Carus · Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Department of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany. kather.jn@tu-dresden.de
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany. kather.jn@tu-dresden.de
Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. kather.jn@tu-dresden.de
Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK. kather.jn@tu-dresden.de
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