Publications by authors named "A M Blitz"
Background: The expansion of tricuspid valve (TV) interventions has underscored the need for accurate and reproducible three-dimensional (3D) transthoracic echocardiographic (TTE) tools for evaluating the tricuspid annulus and for 3D normal values of this structure. The aims of this study were to develop new semi-automated software for 3D TTE analysis of the tricuspid annulus, compare its accuracy and reproducibility against those of multiplanar reconstruction (MPR) reference, and determine normative values.
Methods: Three-dimensional TTE images of 113 patients with variable degrees of tricuspid regurgitation were analyzed using the new semiautomated software and conventional MPR methodology (as the reference standard), each by three independent readers.
AUTOMATED VENTRICLE PARCELLATION AND EVAN'S RATIO COMPUTATION IN PRE- AND POST-SURGICAL VENTRICULOMEGALY.
Proc IEEE Int Symp Biomed Imaging
April 2023
Normal pressure hydrocephalus (NPH) is a brain disorder associated with enlarged ventricles and multiple cognitive and motor symptoms. The degree of ventricular enlargement can be measured using magnetic resonance images (MRIs) and characterized quantitatively using the Evan's ratio (ER). Automatic computation of ER is desired to avoid the extra time and variations associated with manual measurements on MRI.
View Article and Find Full Text PDFInvestigation of probability maps in deep-learning-based brain ventricle parcellation.
Proc SPIE Int Soc Opt Eng
February 2023
Article Synopsis
- Normal Pressure Hydrocephalus (NPH) is a brain disorder involving enlarged ventricles, and precise segmentation of these ventricles from MRI scans is crucial for evaluating patients for surgery.
- This study introduces a modified 3D U-Net model that utilizes probability maps to accurately segment ventricular sub-compartments, even in challenging cases with enlarged ventricles and surgical artifacts.
- The proposed method shows high accuracy, achieving a mean dice similarity coefficient of 0.961 for NPH patients and 0.965 for scans with enlarged ventricles, making it a competitive tool in ventricular system analysis compared to existing methods.
Machine learning techniques designed to recognize views and perform measurements are increasingly used to address the need for automation of the interpretation of echocardiographic images. The current study was designed to determine whether a recently developed and validated deep learning (DL) algorithm for automated measurements of echocardiographic parameters of left heart chamber size and function can improve the reproducibility and shorten the analysis time, compared to the conventional methodology. The DL algorithm trained to identify standard views and provide automated measurements of 20 standard parameters, was applied to images obtained in 12 randomly selected echocardiographic studies.
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