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Robust 3D reconstruction of rib cage bones in computed tomography images, by combining knowledge from radiological, machine learning, and innovative graph guidance

Robust 3D reconstruction of rib cage bones in computed tomography images, by combining knowledge... Purpose More than 70 million computed tomography scans are made per year. A great number of them aim at the thoraxic region, due to the number of organs and structures within it. The 3D visualization of these structures, including the bone, can lead to a more precise medical diagnosis. There are a number of works regarding 3D bone reconstruction, but most fail to present a quantitative evaluation of their assessment or have not achieved an assessment close to 100%. We present an automatic method of bone segmentation followed by 3D reconstruction that approaches these current limitations. Methods The proposed methodology has three blocks: (1) Preprocessing, whereby a median filter was applied to images that presented a high level of noise; (2) feature extraction procedure, in which (i) the images intensity levels were converted to attenuation coefficients and (ii) a (MLP) neural network was used to populate the Space of Attributes with the corresponding feature vectors; and (3) 3D structural construction, whereby a red-and-black tree with graph guidance combined the regarding clustered feature vectors with their spatial neighbors. To evaluate the results, the accuracy between the 2D-segmented images and their corresponding gold standards was calculated. Results The material is composed http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

Robust 3D reconstruction of rib cage bones in computed tomography images, by combining knowledge from radiological, machine learning, and innovative graph guidance

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Publisher
Springer Journals
Copyright
Copyright © 2019 by Sociedade Brasileira de Engenharia Biomedica
Subject
Engineering; Biomedical Engineering and Bioengineering; Biomaterials; Biomedical Engineering/Biotechnology
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-019-00008-z
Publisher site
See Article on Publisher Site

Abstract

Purpose More than 70 million computed tomography scans are made per year. A great number of them aim at the thoraxic region, due to the number of organs and structures within it. The 3D visualization of these structures, including the bone, can lead to a more precise medical diagnosis. There are a number of works regarding 3D bone reconstruction, but most fail to present a quantitative evaluation of their assessment or have not achieved an assessment close to 100%. We present an automatic method of bone segmentation followed by 3D reconstruction that approaches these current limitations. Methods The proposed methodology has three blocks: (1) Preprocessing, whereby a median filter was applied to images that presented a high level of noise; (2) feature extraction procedure, in which (i) the images intensity levels were converted to attenuation coefficients and (ii) a (MLP) neural network was used to populate the Space of Attributes with the corresponding feature vectors; and (3) 3D structural construction, whereby a red-and-black tree with graph guidance combined the regarding clustered feature vectors with their spatial neighbors. To evaluate the results, the accuracy between the 2D-segmented images and their corresponding gold standards was calculated. Results The material is composed

Journal

Research on Biomedical Engineering – Springer Journals

Published: Mar 12, 2019

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