Literature DB >> 19401579

Knee cartilage: efficient and reproducible segmentation on high-spatial-resolution MR images with the semiautomated graph-cut algorithm method.

Hackjoon Shim1, Samuel Chang, Cheng Tao, Jin-Hong Wang, C Kent Kwoh, Kyongtae T Bae.   

Abstract

This HIPAA-compliant study was exempt from institutional review board approval because the 10 image data sets were deidentified in the Osteoarthritis Initiative database, and they were processed and analyzed without any clinical information being accessed. The purpose of this study was to prospectively evaluate the efficiency and reproducibility of the semiautomated graph-cut method (SA method) in the segmentation of knee cartilage and to compare its performance with that of the conventional manual delineation segmentation method (M method). Two radiologists independently performed segmentation with each method in two separate sessions: They performed the M method (M1 and M2 for the first and second sessions, respectively) for every third section and the SA method (SA1 and SA2 for the first and second sessions, respectively) for every section. The SA method was significantly more efficient (mean processing time, 53 minutes vs 156 minutes for SA1 vs M1 and 53 minutes vs 118 minutes for SA2 vs M2; P < .001) and reproducible (mean volume overlap, 94.3% vs 87.8% for the SA method vs the M method; P < .001) than the M method.

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Year:  2009        PMID: 19401579     DOI: 10.1148/radiol.2512081332

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  15 in total

Review 1.  Segmentation of joint and musculoskeletal tissue in the study of arthritis.

Authors:  Valentina Pedoia; Sharmila Majumdar; Thomas M Link
Journal:  MAGMA       Date:  2016-02-25       Impact factor: 2.310

2.  A new technique to evaluate the impact of running on knee cartilage deformation by region.

Authors:  Elora C Brenneman Wilson; Anthony A Gatti; Monica R Maly
Journal:  MAGMA       Date:  2021-01-02       Impact factor: 2.310

3.  Multiatlas-Based Segmentation Editing With Interaction-Guided Patch Selection and Label Fusion.

Authors:  Sang Hyun Park; Yaozong Gao; Dinggang Shen
Journal:  IEEE Trans Biomed Eng       Date:  2015-10-15       Impact factor: 4.538

4.  Quantitative measurement of cartilage volume with automatic cartilage segmentation in knee osteoarthritis.

Authors:  Wenjing Hou; Jun Zhao; Wei Chen; Rui He; Jing Li; Yuan Ou; Mingshan Du; Xuanqi Xiong; Bing Xie; Lian Li; Xiaoyue Zhou; Panli Zuo; Esther Raithel; Zhuoli Zhang
Journal:  Clin Rheumatol       Date:  2020-10-07       Impact factor: 2.980

5.  Fully automated segmentation of cartilage from the MR images of knee using a multi-atlas and local structural analysis method.

Authors:  June-Goo Lee; Serter Gumus; Chan Hong Moon; C Kent Kwoh; Kyongtae Ty Bae
Journal:  Med Phys       Date:  2014-09       Impact factor: 4.071

6.  Deep learning-based fully automatic segmentation of wrist cartilage in MR images.

Authors:  Ekaterina Brui; Aleksandr Y Efimtcev; Vladimir A Fokin; Remi Fernandez; Anatoliy G Levchuk; Augustin C Ogier; Alexey A Samsonov; Jean P Mattei; Irina V Melchakova; David Bendahan; Anna Andreychenko
Journal:  NMR Biomed       Date:  2020-05-11       Impact factor: 4.044

7.  Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry.

Authors:  Berk Norman; Valentina Pedoia; Sharmila Majumdar
Journal:  Radiology       Date:  2018-03-27       Impact factor: 11.105

8.  Composite quantitative knee structure metrics predict the development of accelerated knee osteoarthritis: data from the osteoarthritis initiative.

Authors:  Matthew S Harkey; Julie E Davis; Lori Lyn Price; Robert J Ward; James W MacKay; Charles B Eaton; Grace H Lo; Mary F Barbe; Ming Zhang; Jincheng Pang; Alina C Stout; Bing Lu; Timothy E McAlindon; Jeffrey B Driban
Journal:  BMC Musculoskelet Disord       Date:  2020-05-13       Impact factor: 2.362

9.  A Coarse-to-Fine Framework for Automated Knee Bone and Cartilage Segmentation Data from the Osteoarthritis Initiative.

Authors:  Yang Deng; Lei You; Yanfei Wang; Xiaobo Zhou
Journal:  J Digit Imaging       Date:  2021-05-24       Impact factor: 4.903

10.  pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage.

Authors:  Serena Bonaretti; Garry E Gold; Gary S Beaupre
Journal:  PLoS One       Date:  2020-01-24       Impact factor: 3.240

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