Web-Based Spine Segmentation Using Deep Learning in Computed Tomography Images-Reference-Cited by-同舟云学术

Web-Based Spine Segmentation Using Deep Learning in Computed Tomography Images

Published:2020 Issue:1 Volume:26 Page:61
ISSN:2093-3681
Container-title:Healthcare Informatics Research
language:en
Short-container-title:Healthc Inform Res

Author:

Kim Young Jae¹²³^ORCID,Ganbold Bilegt⁴^ORCID,Kim Kwang Gi¹⁴²³^ORCID

Affiliation:

1. Department of Biomedical Engineering, College of Health Science, Gachon University, Incheon, Korea.

2. Department of Biomedical Engineering, College of Medicine, Gachon Uinversity, Incheon, Korea.

3. Medical Device R&D Center, Biomedical & Convergence Institute, Gachon University Gil Hospital, Incheon, Korea.

4. Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Korea.

Publisher

The Korean Society of Medical Informatics

Subject

Health Information Management,Health Informatics,Biomedical Engineering

Link

https://synapse.koreamed.org/pdf/10.4258/hir.2020.26.1.61

Reference17 articles.

1. Mayo Clinic. Back pain [Internet]. Scottsdale (AZ). Mayo Clinic. cited at 2020 Jan 31. Available from: https://www.mayoclinic.org/diseases-conditions/backpain/symptoms-causes/syc-20369906

2. Computer-Aided Diagnosis and Artificial Intelligence in Clinical Imaging

3. Automatic spine segmentation using convolutional neural network via redundant generation of class labels for 3D spine modeling [Internet]. Ithaca (NY). arXiv.org. 2017. cited at 2020 Jan 31. Available from: https://arxiv.org/abs/1712.01640

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