Literature DB >> 31946384

Deep-learning-based fully automatic spine centerline detection in CT data.

Roman Jakubicek, Jiri Chmelik, Petr Ourednicek, Jiri Jan.   

Abstract

In this contribution, we present a fully automatic approach, that is based on two convolution neural networks (CNN) together with a spine tracing algorithm utilizing a population optimization algorithm. Based on the evaluation of 130 CT scans including heavily distorted and complicated cases, it turned out that this new combination enables fast and robust detection with almost 90% of correctly determined spinal centerlines with computing time of fewer than 20 seconds.

Entities:  

Year:  2019        PMID: 31946384     DOI: 10.1109/EMBC.2019.8856528

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  2 in total

1.  A Review on the Use of Artificial Intelligence in Spinal Diseases.

Authors:  Parisa Azimi; Taravat Yazdanian; Edward C Benzel; Hossein Nayeb Aghaei; Shirzad Azhari; Sohrab Sadeghi; Ali Montazeri
Journal:  Asian Spine J       Date:  2020-04-24

2.  Evaluation of Deep Learning-Based Automated Detection of Primary Spine Tumors on MRI Using the Turing Test.

Authors:  Hanqiang Ouyang; Fanyu Meng; Jianfang Liu; Xinhang Song; Yuan Li; Yuan Yuan; Chunjie Wang; Ning Lang; Shuai Tian; Meiyi Yao; Xiaoguang Liu; Huishu Yuan; Shuqiang Jiang; Liang Jiang
Journal:  Front Oncol       Date:  2022-03-11       Impact factor: 6.244

  2 in total

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