Deep Learning and Computer Vision Techniques for Automated Total Hip Arthroplasty Planning on 2-D Radiographs-Reference-Cited by-同舟云学术

Deep Learning and Computer Vision Techniques for Automated Total Hip Arthroplasty Planning on 2-D Radiographs

Published:2022 Issue: Volume:10 Page:94145-94157
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Kim Minwoo¹,Oh Il-Seok¹^ORCID,Yoon Sun-Jung²

Affiliation:

1. Division of Computer Science and Engineering, Jeonbuk National University, Jeonju, South Korea

2. Department of Orthopedic Surgery, Medical School, Jeonbuk National University, Jeonju, South Korea

Funder

Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education

Grant of the Korea Health Technology Research and Development Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea

Fund of Biomedical Research Institute, Joenbuk National University Hospital

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09875293.pdf?arnumber=9875293

Reference64 articles.

1. Three-Dimensional Feature-Enhanced Network for Automatic Femur Segmentation

2. Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks

3. Graph-Cut-Based Segmentation of Proximal Femur from Computed Tomography Images with Shape Prior

4. An articulated statistical shape model for accurate hip joint segmentation

5. Automated CT Segmentation of Diseased Hip Using Hierarchical and Conditional Statistical Shape Models

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