CervSpineNet: a hybrid deep learning-based approach for the segmentation of cervical spinous processes.
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| Title: | CervSpineNet: a hybrid deep learning-based approach for the segmentation of cervical spinous processes. |
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| Authors: | Sawant JS; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States., Moukheiber L; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.; Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, United States., Nair A; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.; School of Computer Science, Georgia Institute of Technology, Atlanta, GA, United States., Mahajan A; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.; School of Computer Science, Georgia Institute of Technology, Atlanta, GA, United States., Byun J; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States., Pichaimani I; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States., Yoon ST; Department of Orthopedic Surgery, Emory University, Atlanta, GA, United States., Martin CT; Department of Orthopedic Surgery, University of Minnesota, Minneapolis, MN, United States., Mitchell CS; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.; Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, United States. |
| Source: | Frontiers in bioengineering and biotechnology [Front Bioeng Biotechnol] 2026 Jan 19; Vol. 13, pp. 1733689. Date of Electronic Publication: 2026 Jan 19 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Frontiers Media S.A Country of Publication: Switzerland NLM ID: 101632513 Publication Model: eCollection Cited Medium: Print ISSN: 2296-4185 (Print) Linking ISSN: 22964185 NLM ISO Abbreviation: Front Bioeng Biotechnol Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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