Assisting Radiologists Through Automatic Cardiothoracic Ratio Calculation

2020 ◽  
Author(s):  
Bogdan Bercean ◽  
Stefan Iarca ◽  
Andrei Tenescu ◽  
Cristian Avramescu ◽  
Sebastian Fuicu
Keyword(s):  
Cardiothoracic Ratio ◽  
Ratio Calculation

scholarly journals Automatic Cardiothoracic Ratio Calculation With Deep Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 37749-37756 ◽  
Author(s):  
Zhennan Li ◽  
Zhihui Hou ◽  
Chen Chen ◽  
Zhi Hao ◽  
Yunqiang An ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiothoracic Ratio ◽  
Ratio Calculation

scholarly journals Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mu Sook Lee ◽  
Yong Soo Kim ◽  
Minki Kim ◽  
Muhammad Usman ◽  
Shi Sub Byon ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Absolute Error ◽  
Training Dataset ◽  
Computer Aided Detection ◽  
Test Dataset ◽  
Cardiothoracic Ratio ◽  
Computer Aided ◽  
Chest X Ray ◽  
Public Datasets

AbstractWe examined the feasibility of explainable computer-aided detection of cardiomegaly in routine clinical practice using segmentation-based methods. Overall, 793 retrospectively acquired posterior–anterior (PA) chest X-ray images (CXRs) of 793 patients were used to train deep learning (DL) models for lung and heart segmentation. The training dataset included PA CXRs from two public datasets and in-house PA CXRs. Two fully automated segmentation-based methods using state-of-the-art DL models for lung and heart segmentation were developed. The diagnostic performance was assessed and the reliability of the automatic cardiothoracic ratio (CTR) calculation was determined using the mean absolute error and paired t-test. The effects of thoracic pathological conditions on performance were assessed using subgroup analysis. One thousand PA CXRs of 1000 patients (480 men, 520 women; mean age 63 ± 23 years) were included. The CTR values derived from the DL models and diagnostic performance exhibited excellent agreement with reference standards for the whole test dataset. Performance of segmentation-based methods differed based on thoracic conditions. When tested using CXRs with lesions obscuring heart borders, the performance was lower than that for other thoracic pathological findings. Thus, segmentation-based methods using DL could detect cardiomegaly; however, the feasibility of computer-aided detection of cardiomegaly without human intervention was limited.

scholarly journals Utility of Recipient Cardiothoracic Ratio in Predicting Delayed Chest Closure after Heart Transplantation

2021 ◽  
Vol 40 (4) ◽  
pp. S260-S261
Author(s):  
A. Sukhavasi ◽  
M.P. Weber ◽  
E.J. Maynes ◽  
Y. Brailovsky ◽  
M.K. Shah ◽  
...  
Keyword(s):  
Heart Transplantation ◽  
Cardiothoracic Ratio

Fetal sonographic cardiothoracic ratio at midpregnancy as a predictor of Hb Bart disease.

1999 ◽  
Vol 18 (12) ◽  
pp. 807-811 ◽  
Author(s):  
T Tongsong ◽  
C Wanapirak ◽  
S Sirichotiyakul ◽  
W Piyamongkol ◽  
P Chanprapaph
Keyword(s):  
Cardiothoracic Ratio

scholarly journals Intensity ratios for XDR/PDR identification

2015 ◽  
Vol 11 (S315) ◽  
Author(s):  
C.-E. Green ◽  
M. R. Cunningham ◽  
J. A. Green ◽  
J. R. Dawson ◽  
P. A. Jones ◽  
...  
Keyword(s):  
Black Holes ◽  
Star Formation ◽  
Active Galactic Nuclei ◽  
Intensity Ratio ◽  
Galactic Nuclei ◽  
Relative Influence ◽  
Calculation Methods ◽  
X Ray ◽  
Ratio Calculation ◽  
Intensity Ratios

AbstractThe intensity ratios of HCO+/HCN and HNC/HCN (1-0) reveal the relative influence of star formation and active galactic nuclei (AGN) or black holes on the circum-nuclear gas of a galaxy, allowing the identification of X-ray dominated regions (XDRs) and Photon-dominated regions (PDRs). It is not always clear in the literature how this intensity ratio calculation has been, or should be performed. This paper discusses ratio calculation methods for interferometric data.

scholarly journals Assessment of the vertebral heart scale and cardiothoracic ratio to standardize the heart size of collared peccaries (Tayassu tajacu Linnaeus, 1758) restrained with ketamine and midazolam

2018 ◽  
Vol 38 (8) ◽  
pp. 1705-1711
Author(s):  
Osmar F. Silva-Filho ◽  
Gerson T. Pessoa ◽  
Francisco C.A. Sousa ◽  
Renan P.S. Rodrigues ◽  
Laecio S. Moura ◽  
...  
Keyword(s):  
Gender Differences ◽  
Digital Radiography ◽  
Wild Species ◽  
Diagnostic Tools ◽  
Mean Values ◽  
Cardiothoracic Ratio ◽  
Heart Size ◽  
Cardiac Silhouette ◽  
Collared Peccary ◽  
Tayassu Tajacu

ABSTRACT: Wild species, especially those threatened with extinction, are increasingly being investigated to obtain information that can be useful for their preservation. The objective of the present study was to standardize the vertebral heart scale (VHS) and cardiothoracic ratio (CTR) of the collared peccary (Tayassu tajacu Linnaeus, 1758) sedated with ketamine and midazolam. Fourteen clinically healthy collared peccaries were examined in the two-year age group weighing 15-22kg. The animals were submitted to digital radiography of the thorax in lateral and dorsal ventral projections to calculate the VHS and CTR. The VHS mean values for males and females was 8.88±0.51v for right recumbency and 8.84±0.39v for left decubitus, and there were no significant between-gender differences regarding recumbency (p>0.05). The CTR showed mean values of 0.50±0.05 (males) and 0.45±0.04 (females), but the gender-differences were not significant (p>0.05). A positive correlation was shown between VHS and CTR (r=0.98, right decubitus; r=0.96, left decubitus). Establishing reference values for heart measurements in collared peccaries using digital radiography of the thorax permitted standardization of the VHS and CTR values for this wild species. In the studied wild animal model, the VHS and CTR heart assessment indexes were shown to be essential diagnostic tools for investigations of alterations in the size of the cardiac silhouette.

scholarly journals Count-based method for specific binding ratio calculation in [I-123]FP-CIT SPECT analysis

2018 ◽  
Vol 33 (1) ◽  
pp. 14-21 ◽  
Author(s):  
Mahmudur G. M. Rahman ◽  
Muhammad M. Islam ◽  
Tetsuya Tsujikawa ◽  
Yasushi Kiyono ◽  
Hidehiko Okazawa
Keyword(s):  
Specific Binding ◽  
Specific Binding Ratio ◽  
Binding Ratio ◽  
Ratio Calculation
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