Automatic detection and segmentation of ischemic lesions in computed tomography images of stroke patients

2013 ◽  
Author(s):  
Pieter C. Vos ◽  
J. Matthijs Biesbroek ◽  
Nick A. Weaver ◽  
Birgitta K. Velthuis ◽  
Max A. Viergever
Keyword(s):  
Computed Tomography ◽  
Automatic Detection ◽  
Stroke Patients ◽  
Computed Tomography Images

scholarly journals Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

2017 ◽  
Vol 42 ◽  
pp. 1-13 ◽  
Author(s):  
Arnaud Arindra Adiyoso Setio ◽  
Alberto Traverso ◽  
Thomas de Bel ◽  
Moira S.N. Berens ◽  
Cas van den Bogaard ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Pulmonary Nodules ◽  
Automatic Detection ◽  
Computed Tomography Images

Automatic detection of spiculation of pulmonary nodules in computed tomography images

2015 ◽  
Author(s):  
F. Ciompi ◽  
C. Jacobs ◽  
E. T. Scholten ◽  
S. J. van Riel ◽  
M. M. W. Wille ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Pulmonary Nodules ◽  
Automatic Detection ◽  
Computed Tomography Images

scholarly journals e-ASPECTS software is non-inferior to neuroradiologists in applying the ASPECT score to computed tomography scans of acute ischemic stroke patients

2016 ◽  
Vol 12 (6) ◽  
pp. 615-622 ◽  
Author(s):  
Simon Nagel ◽  
Devesh Sinha ◽  
Diana Day ◽  
Wolfgang Reith ◽  
René Chapot ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Ischemic Stroke ◽  
Correlation Coefficients ◽  
Ground Truth ◽  
Stroke Patients ◽  
Computed Tomography Images ◽  
Contrast Enhanced ◽  
Enhanced Computed Tomography ◽  
Sensitivity Specificity ◽  
Contrast Enhanced Computed Tomography

Background The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is an established 10-point quantitative topographic computed tomography scan score to assess early ischemic changes. We performed a non-inferiority trial between the e-ASPECTS software and neuroradiologists in scoring ASPECTS on non-contrast enhanced computed tomography images of acute ischemic stroke patients. Methods In this multicenter study, e-ASPECTS and three independent neuroradiologists retrospectively and blindly assessed baseline non-contrast enhanced computed tomography images of 132 patients with acute anterior circulation ischemic stroke. Follow-up scans served as ground truth to determine the definite area of infarction. Sensitivity, specificity, and accuracy for region- and score-based analysis, receiver-operating characteristic curves, Bland-Altman plots and Matthews correlation coefficients relative to the ground truth were calculated and comparisons were made between neuroradiologists and different pre-specified e-ASPECTS operating points. The non-inferiority margin was set to 10% for both sensitivity and specificity on region-based analysis. Results In total 2640 (132 patients × 20 regions per patient) ASPECTS regions were scored. Mean time from onset to baseline computed tomography was 146 ± 124 min and median NIH Stroke Scale (NIHSS) was 11 (6–17, interquartile range). Median ASPECTS for ground truth on follow-up imaging was 8 (6.5–9, interquartile range). In the region-based analysis, two e-ASPECTS operating points (sensitivity, specificity, and accuracy of 44%, 93%, 87% and 44%, 91%, 85%) were statistically non-inferior to all three neuroradiologists (all p-values <0.003). Both Matthews correlation coefficients for e-ASPECTS were higher (0.36 and 0.34) than those of all neuroradiologists (0.32, 0.31, and 0.3). Conclusions e-ASPECTS was non-inferior to three neuroradiologists in scoring ASPECTS on non-contrast enhanced computed tomography images of acute stroke patients.

Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images

2009 ◽  
Vol 39 (10) ◽  
pp. 921-933 ◽  
Author(s):  
Jorge Juan Suárez-Cuenca ◽  
Pablo G. Tahoces ◽  
Miguel Souto ◽  
María J. Lado ◽  
Martine Remy-Jardin ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Pulmonary Nodules ◽  
Automatic Detection ◽  
Computed Tomography Images

scholarly journals Semi-automatic detection of hepatic tumor in computed tomography images

2019 ◽  
Vol 1408 ◽  
pp. 012001
Author(s):  
F Sáenz ◽  
M Vera ◽  
J López ◽  
Y Huérfano ◽  
O Valbuena ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Automatic Detection ◽  
Hepatic Tumor ◽  
Computed Tomography Images
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