Literature DB >> 27065241

Quantitative normal thoracic anatomy at CT.

Monica M S Matsumoto1, Jayaram K Udupa2, Yubing Tong1, Babak Saboury1, Drew A Torigian1.   

Abstract

Automatic anatomy recognition (AAR) methodologies for a body region require detailed understanding of the morphology, architecture, and geographical layout of the organs within the body region. The aim of this paper was to quantitatively characterize the normal anatomy of the thoracic region for AAR. Contrast-enhanced chest CT images from 41 normal male subjects, each with 11 segmented objects, were considered in this study. The individual objects were quantitatively characterized in terms of their linear size, surface area, volume, shape, CT attenuation properties, inter-object distances, size and shape correlations, size-to-distance correlations, and distance-to-distance correlations. A heat map visualization approach was used for intuitively portraying the associations between parameters. Numerous new observations about object geography and relationships were made. Some objects, such as the pericardial region, vary far less than others in size across subjects. Distance relationships are more consistent when involving an object such as trachea and bronchi than other objects. Considering the inter-object distance, some objects have a more prominent correlation, such as trachea and bronchi, right and left lungs, arterial system, and esophagus. The proposed method provides new, objective, and usable knowledge about anatomy whose utility in building body-wide models toward AAR has been demonstrated in other studies.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Automatic anatomy recognition; CT; Quantification; Quantitative radiology; Segmentation; Thorax

Mesh:

Year:  2016        PMID: 27065241     DOI: 10.1016/j.compmedimag.2016.03.005

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  2 in total

1.  Body region localization in whole-body low-dose CT images of PET/CT scans using virtual landmarks.

Authors:  Peirui Bai; Jayaram K Udupa; Yubing Tong; ShiPeng Xie; Drew A Torigian
Journal:  Med Phys       Date:  2019-01-24       Impact factor: 4.071

2.  AAR-RT - A system for auto-contouring organs at risk on CT images for radiation therapy planning: Principles, design, and large-scale evaluation on head-and-neck and thoracic cancer cases.

Authors:  Xingyu Wu; Jayaram K Udupa; Yubing Tong; Dewey Odhner; Gargi V Pednekar; Charles B Simone; David McLaughlin; Chavanon Apinorasethkul; Ontida Apinorasethkul; John Lukens; Dimitris Mihailidis; Geraldine Shammo; Paul James; Akhil Tiwari; Lisa Wojtowicz; Joseph Camaratta; Drew A Torigian
Journal:  Med Image Anal       Date:  2019-01-29       Impact factor: 8.545

  2 in total

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