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Shoulder Joint Image Segmentation Based on Joint Convolutional Neural Networks

Published: 20 September 2019 Publication History

Abstract

Magnetic resonance imaging (MRI) is now commonly used for the examination and diagnosis of joints. A key step is to segment the bones of interest in MRI. This paper presents an algorithm for automatic segmentation of shoulder joint images based on a joint convolutional neural network model, which can accurately segment glenoid and humeral head in the shoulder image. This method includes two collaborative deep learning networks. The first network uses Mask R-CNN segmentation model to perform preliminary instance segmentation of glenoid and humeral head. The second network uses the probability maps of voxel belonging to the different objects (glenoid, humeral head, and background) as the constraint of the spatial location; thereby more accurate segmentation can be obtained. There are 50 groups of MRI which are used to train and test, the accuracy of Dice Coefficient, Positive Predicted Value (PPV), and Sensitivity for glenoid and humeral head reached 0.91±0.02, 0.95±0.01, 0.94±0.02 and 0.88±0.01, 0.91±0.02, 0.90±0.02 respectively, exceeding the current advanced segmentation algorithms.

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Cited By

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  • (2022)An Overview of Biomedical Image Analysis From the Deep Learning PerspectiveResearch Anthology on Improving Medical Imaging Techniques for Analysis and Intervention10.4018/978-1-6684-7544-7.ch003(43-59)Online publication date: 9-Sep-2022
  • (2021)A Role of Machine Learning and Deep Learning Techniques for Preoperative Prediction in Shoulder Arthroplasty: SurveyComputational Intelligence in Pattern Recognition10.1007/978-981-16-2543-5_17(195-204)Online publication date: 5-Sep-2021
  • (2020)An Overview of Biomedical Image Analysis From the Deep Learning PerspectiveApplications of Advanced Machine Intelligence in Computer Vision and Object Recognition10.4018/978-1-7998-2736-8.ch008(197-218)Online publication date: 2020

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  1. Shoulder Joint Image Segmentation Based on Joint Convolutional Neural Networks

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    cover image ACM Other conferences
    RICAI '19: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence
    September 2019
    803 pages
    ISBN:9781450372985
    DOI:10.1145/3366194
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 September 2019

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    Author Tags

    1. Convolutional neural networks
    2. Image segmentation
    3. Mask R-CNN
    4. Medical image analysis

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    RICAI '19 Paper Acceptance Rate 140 of 294 submissions, 48%;
    Overall Acceptance Rate 140 of 294 submissions, 48%

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    View all
    • (2022)An Overview of Biomedical Image Analysis From the Deep Learning PerspectiveResearch Anthology on Improving Medical Imaging Techniques for Analysis and Intervention10.4018/978-1-6684-7544-7.ch003(43-59)Online publication date: 9-Sep-2022
    • (2021)A Role of Machine Learning and Deep Learning Techniques for Preoperative Prediction in Shoulder Arthroplasty: SurveyComputational Intelligence in Pattern Recognition10.1007/978-981-16-2543-5_17(195-204)Online publication date: 5-Sep-2021
    • (2020)An Overview of Biomedical Image Analysis From the Deep Learning PerspectiveApplications of Advanced Machine Intelligence in Computer Vision and Object Recognition10.4018/978-1-7998-2736-8.ch008(197-218)Online publication date: 2020

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