Segmenting Whole-Body MRI and CT for Multiorgan Anatomic Structure Delineation.
Authors
Affiliations (21)
Affiliations (21)
- Department of Radiology, Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the Netherlands.
- Department of Diagnostic and Interventional Radiology, School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Mass.
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany.
- Laboratory for Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany.
- Chair for AI in Medicine and Healthcare, Klinikum rechts der Isar, Technical University Munich, Munich, Germany.
- Department of Computing, Imperial College London, London, UK.
- Institute for Advanced Study, Technical University Munich, Munich, Germany.
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Mass.
- Departments of Radiation Oncology and Radiology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, Mass.
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands.
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany.
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
- Clinic for Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany.
- Department of Diagnostic and Interventional Radiology and Neuroradiology, Universitätsklinikum Augsburg, Augsburg, Germany.
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
- Experimental Clinical Research Center, Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
- Department of Cardiovascular Radiology and Nuclear Medicine, School of Medicine and Health, German Heart Center, TUM University Hospital, Technical University of Munich, Lazarettstr 36, 80636 Munich, Germany.
Abstract
<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To develop and validate MRSegmentator, a retrospective cross-modality deep learning model for multiorgan segmentation of MRI scans. Materials and Methods This retrospective study trained MRSegmentator on 1,200 manually annotated UK Biobank Dixon MRI sequences (50 participants), 221 in-house abdominal MRI sequences (177 patients), and 1228 CT scans from the TotalSegmentator-CT dataset. A human-in-the-loop annotation workflow leveraged cross-modality transfer learning from an existing CT segmentation model to segment 40 anatomic structures. The model's performance was evaluated on 900 MRI sequences from 50 participants in the German National Cohort (NAKO), 60 MRI sequences from AMOS22 dataset, and 29 MRI sequences from TotalSegmentator-MRI. Reference standard manual annotations were used for comparison. Metrics to assess segmentation quality included Dice Similarity Coefficient (DSC). Statistical analyses included organ-and sequence-specific mean ± SD reporting and two-sided <i>t</i> tests for demographic effects. Results 139 participants were evaluated; demographic information was available for 70 (mean age 52.7 years ± 14.0 [SD], 36 female). Across all test datasets, MRSegmentator demonstrated high class wise DSC for well-defined organs (lungs: 0.81-0.96, heart: 0.81-0.94) and organs with anatomic variability (liver: 0.82-0.96, kidneys: 0.77-0.95). Smaller structures showed lower DSC (portal/splenic veins: 0.64-0.78, adrenal glands: 0.56-0.69). The average DSC on the external testing using NAKO data, ranged from 0.85 ± 0.08 for T2-HASTE to 0.91 ± 0.05 for in-phase sequences. The model generalized well to CT, achieving mean DSC of 0.84 ± 0.12 on AMOS CT data. Conclusion MRSegmentator accurately segmented 40 anatomic structures on MRI and generalized to CT; outperforming existing open-source tools. Published under a CC BY 4.0 license.