ArticleScientific reports2018
Unsupervised Medical Image Segmentation Based on the Local Center of Mass.
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23 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- A transformer-based pyramid network for coronary calcified plaque segmentation in intravascular optical coherence tomography images.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
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- Multi-Atlas Image Soft Segmentation via Computation of the Expected Label Value.IEEE transactions on medical imaging · 2021Article
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- Fracture reduction planning and guidance in orthopaedic trauma surgery via multi-body image registration.Medical image analysis · 2021Article
- A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification.Computational intelligence and neuroscience · 2021Article
- Three-Dimensional Whole-Organ Characterization of the Regional Alveolar Morphology in Normal Murine Lungs.Frontiers in physiology · 2021Article
- [Joint optic disc and cup segmentation based on residual multi-scale fully convolutional neural network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Article
- CTumorGAN: a unified framework for automatic computed tomography tumor segmentation.European journal of nuclear medicine and molecular imaging · 2020Article
- Coronary angiography video segmentation method for assisting cardiovascular disease interventional treatment.BMC medical imaging · 2020Article
- Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering.Computers in biology and medicine · 2020Article
- Three-Plane-assembled Deep Learning Segmentation of Gliomas.Radiology. Artificial intelligence · 2020Article
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Authors and funding
4 authors.
Iman AganjDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. iman@nmr.mgh.harvard.edu.ORCID 0000-0002-4673-1293
Mukesh G HarisinghaniDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Ralph WeisslederDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Bruce FischlDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Funding
The VETSA Longitudinal Twin Study of Cognition and Aging (VETSA 4)R01AG050595 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ELMAN, JEREMY A, FRANZ, CAROL ELAINE · 2015 to 2024
$28.0MThe VETSA Longitudinal MRI Twin Study of AgingR01AG022381 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KREMEN, WILLIAM S. · 2003 to 2019
$22.3MEPIDEMIOLOGY OF DEMENTIA IN THE FRAMINGHAM STUDYR01AG008122 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA, SESHADRI, SUDHA · 1989 to 2015
$14.4MMRI, Genetics &Cognitive Precursors of AD &DementiaR01AG016495 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA · 1999 to 2016
$12.5MThe Human Connectome Project (HCP)U01MH093765 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2010 to 2014
$12.1MProject 4P41EB015896 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2012 to 2018
$9.8MThe VETSA Longitudinal MRI Twin Study of Aging (VETSA MRI 4)R01AG076838 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ANDERS M DALE, Jeremy A Elman · 2022 to 2026
$8.7MNeuropathology of CTE and Delayed Effects of TBI: Toward In-Vivo DiagnosticsU01NS086625 · NINDS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DAMS-O'CONNOR, KRISTEN · 2014 to 2017
$6.1MA Longitudinal Analysis Stream for FreeSurferR01NS083534 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Bruce Fischl · 2014 to 2026
$4.6MOrchestra: A high performance biomedical supercomputing collaborativeS10RR028832 · NCRR · HARVARD MEDICAL SCHOOL · PI DANUSER, GAUDENZ · 2010 to 2010
$3.7MTools for Analyzing Cortical Structure and ConnectivityR01NS052585 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE · 2006 to 2010
$2.5MAlgorithms for MR and OCT-based Architectonic and Laminar SegmentationR01EB019956 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE · 2015 to 2018
$2.4MBrightFocus Foundation A2016172SNCCIH NIH HHS RC1 AT005728NCRR NIH HHS S10 RR019307NCRR NIH HHS S10 RR023043NCRR NIH HHS S10 RR023401NCRR NIH HHS S10 RR028832NIA NIH HHS R01 AG008122NIA NIH HHS R01 AG016495NIA NIH HHS R01 AG022381NIA NIH HHS R01 AG050595NIA NIH HHS R01 AG076838NIBIB NIH HHS P41 EB015896NIBIB NIH HHS R01 EB006758NIBIB NIH HHS R01 EB019956NIBIB NIH HHS R21 EB018907NIDDK NIH HHS K01 DK101631NIDDK NIH HHS R21 DK108277NIMH NIH HHS U01 MH093765NINDS NIH HHS R01 NS052585NINDS NIH HHS R01 NS070963NINDS NIH HHS R01 NS083534NINDS NIH HHS R21 NS072652NINDS NIH HHS U01 NS086625Nvidia GPU GrantU.S. Department of Health & Human Services | National Institutes of Health (NIH) S10RR019307U.S. Department of Health & Human Services | National Institutes of Health (NIH) S10RR023043U.S. Department of Health & Human Services | National Institutes of Health (NIH) S10RR028832U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) P41EB015896U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) R01EB006758U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) R01EB019956U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) R21EB018907U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) K01DK101631U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) R21DK108277U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) R01NS052585U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) R01NS070963U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) R01NS083534U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) R21NS072652U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) U01NS086625U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) 5R01AG008122-22U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) AG022381U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) R01AG016495U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) R01AG016495-11U.S. Department of Health & Human Services | NIH | NIH Blueprint for Neuroscience Research U01MH093765
Abstract
Image segmentation is a critical step in numerous medical imaging studies, which can be facilitated by automatic computational techniques. Supervised methods, although highly effective, require large training datasets of manually labeled images that are labor-intensive to produce. Unsupervised methods, on the contrary, can be used in the absence of training data to segment new images. We introduce a new approach to unsupervised image segmentation that is based on the computation of the local center of mass. We propose an efficient method to group the pixels of a one-dimensional signal, which we then use in an iterative algorithm for two- and three-dimensional image segmentation. We validate our method on a 2D X-ray image, a 3D abdominal magnetic resonance (MR) image and a dataset of 3D cardiovascular MR images.
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AutomationDiagnostic ImagingHumansImage Processing, Computer-Assisted
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