arXiv subjects

Shelby Kutty

Publications and source records attributed to Shelby Kutty.

3 recordsLinked to original sources

arXiv2021

Direct estimation of global longitudinal strain from echocardiograms using a logarithm-scaled Fourier magnitude correlation

We present a new method for measuring global longitudinal strain and global longitudinal strain rate from 2D echocardiograms using a logarithmic-transform correlation (LTC) method. In contrast to traditional echocardiography strain analysis, our approach does not require cardiac chamber segmentation and regularization. The algorithm was benchmarked against two conventional strain analysis methods using synthetic left ventricle ultrasound images. Measurement error was assessed as a function of contrast-to-noise ratio (CNR) using mean absolute error and root-mean-square error and showed better agreement to the ground truth for strain($R^2$ = 0.91) and strain rate($R^2$ = 0.85) as compared to conventional algorithms (strain($R^2$ = 0.7), strain rate($R^2$ = 0.7)). Also, our method was unaffected by CNR. A 200% increase in strain measurement accuracy was observed compared to the conventional algorithms. Subsequently, we tested the method using a 54-subject clinical cohort (20 subjects diseased with cardiomyopathy, 34 healthy controls). Our method distinguished between normal and abnormal left ventricular function with an AUC = 0.85, a 10% improvement over the conventional GLS algorithms.

physics.med-ph↗

arXiv2021

Deep Learning-based Automated Aortic Area and Distensibility Assessment: The Multi-Ethnic Study of Atherosclerosis (MESA)

This study applies convolutional neural network (CNN)-based automatic segmentation and distensibility measurement of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging (PC-cine MRI) within the large MESA cohort with subsequent assessment on an external cohort of thoracic aortic aneurysm (TAA) patients. 2D PC-cine MRI images of the ascending and descending aorta at the pulmonary artery bifurcation from the MESA study were included. Train, validation, and internal test sets consisted of 1123 studies (24282 images), 374 studies (8067 images), and 375 studies (8069 images), respectively. An external test set of TAAs consisted of 37 studies (3224 images). A U-Net based CNN was constructed, and performance was evaluated utilizing dice coefficient (for segmentation) and concordance correlation coefficients (CCC) of aortic geometric parameters by comparing to manual segmentation and parameter estimation. Dice coefficients for aorta segmentation were 97.6% (CI: 97.5%-97.6%) and 93.6% (84.6%-96.7%) on the internal and external test of TAAs, respectively. CCC for comparison of manual and CNN maximum and minimum ascending aortic areas were 0.97 and 0.95, respectively, on the internal test set and 0.997 and 0.995, respectively, for the external test. CCCs for maximum and minimum descending aortic areas were 0.96 and 0. 98, respectively, on the internal test set and 0.93 and 0.93, respectively, on the external test set. We successfully developed and validated a U-Net based ascending and descending aortic segmentation and distensibility quantification model in a large multi-ethnic database and in an external cohort of TAA patients.

q-bio.QM↗

arXiv2020

Unsupervised Segmentation of B-Mode Echocardiograms

We present a method for unsupervised segmentation of echocardiograms (echo). The method uses an iterative Dijkstra's algorithm, a strategic node selection, and a novel cost matrix formulation based on intensity peak prominence and is thus termed the "Prominence Iterative Dijkstra's" algorithm, or ProID. Although the current analysis focuses on the left ventricle (LV), ProID is applicable to all four heart chambers. ProID was tested using artificial echo images representing five different systems. Results showed accurate LV contours and volume estimations as compared to the ground-truth for all systems. Subsequently, ProID was used to analyze a clinical cohort of 66 pediatric patients, including both normal and diseased hearts. Output segmentations, end-diastolic, end-systolic volumes, and ejection fraction (EF) were compared against manual segmentations from two expert readers. ProID maintained an average Dice similarity score of 0.93 when comparing against manual segmentation. Comparing the two expert readers, the manual segmentations maintained a score of 0.93, which increased to 0.95 when they used ProID. Thus, ProID successfully reduced the inter-operator variability across the two expert readers. Overall, this work demonstrates that ProID yields accurate boundaries across all age groups, disease states, and echo platforms with low computation cost, thereby establishing its clinical usefulness.

eess.IV↗