Literature DB >> 19963589

Automated recognition of the psoas major muscles on X-ray CT images.

N Kamiya1, X Zhou, H Chen, T Hara, H Hoshi, R Yokoyama, M Kanematsu, H Fujita.   

Abstract

The purpose of this study is to recognize the psoas major muscle on X-ray CT images. For this purpose, we propose a novel recognition method. The recognition process in this method involves three steps: the generation of a shape model for the psoas major muscle, recognition of anatomical points such as the origin and insertion, and the recognition of the psoas major muscles by the use of the shape model. We generated the shape model using 20 CT cases and tested the model for recognition in 20 other CT cases. The average Jaccard similarity coefficient (JSC) and reproducibility rate were 0.704 and 0.783, respectively. Experimental results indicate that our method was effective for a 2-D cross-sectional area (CSA) analysis.

Mesh:

Year:  2009        PMID: 19963589     DOI: 10.1109/IEMBS.2009.5332597

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


  3 in total

1.  Muscle segmentation in axial computed tomography (CT) images at the lumbar (L3) and thoracic (T4) levels for body composition analysis.

Authors:  Setareh Dabiri; Karteek Popuri; Elizabeth M Cespedes Feliciano; Bette J Caan; Vickie E Baracos; Mirza Faisal Beg
Journal:  Comput Med Imaging Graph       Date:  2019-05-09       Impact factor: 4.790

2.  Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications.

Authors:  Naoki Kamiya; Jing Li; Masanori Kume; Hiroshi Fujita; Dinggang Shen; Guoyan Zheng
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-09-01       Impact factor: 2.924

3.  Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis.

Authors:  Hyunkwang Lee; Fabian M Troschel; Shahein Tajmir; Georg Fuchs; Julia Mario; Florian J Fintelmann; Synho Do
Journal:  J Digit Imaging       Date:  2017-08       Impact factor: 4.056

  3 in total

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