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Johan M. Thijssen

Researcher at Radboud University Nijmegen

Publications -  149
Citations -  3814

Johan M. Thijssen is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 36, co-authored 148 publications receiving 3640 citations. Previous affiliations of Johan M. Thijssen include Radboud University Nijmegen Medical Centre & Boston Children's Hospital.

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Journal ArticleDOI

Detection and differentiation of diffuse liver disease by quantitative echography. A retrospective assessment.

TL;DR: The detection of diffuse liver diseases can be based on echographic parameters, related to a diffuse scattering model, whereas the differentiation among diseases needs additional parameters derived from a structural scattering model.
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Echographic differentiation of histological types of intraocular melanoma.

TL;DR: The aim of this study was to assess the differentiation of histologically different types of choroidal melanomas by means of clinical and quantitative acoustic/texture parameters of echograms by a skilled diagnostician.
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Transcutaneous vs. intraoperative quantitative ultrasound for staging bovine hepatic steatosis.

TL;DR: Testing the hypothesis that the CAUS method can predict the liver fat content with similar accuracy and precision as with intraoperative (Intraop) US images found equivalent high predictive values for AUC, sensitivity and specificity for Intraop and Transc applications were found.
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Persistent Reduction in Left Ventricular Strain Using Two-Dimensional Speckle-Tracking Echocardiography after Balloon Valvuloplasty in Children with Congenital Valvular Aortic Stenosis

TL;DR: Shortly after balloon valvuloplasty for severe congenital VAS, there is an improvement in systolic myocardial deformation, but 2DSTE parameters do not return to normal at 3-year follow-up, and cannot be fully attributed to residual stenosis or aortic regurgitation.
Proceedings ArticleDOI

2H-1 In Vivo 3D Cardiac and Skeletal Muscle Strain Estimation

TL;DR: In this paper, a coarse-to-fine iterative 2D strain algorithm using spatial correction and local stretching was implemented for measuring sub-sample and sub-line displacements in 3D X4 matrix array transducers.