Naslov
Estimation of the Left Ventricle Volume using Semantic Segmentation

Autori
Habijan, Marija ; Leventić, Hrvoje ; Galić, Irena ; Babin, Danilo

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
ELMAR 2019 / - Zadar, 2019, 39-44

Skup
61st International Symposium Electronics in Marine (ELMAR 2019)

Mjesto i datum
Zadar, Hrvatska, 23.09.2019. - 25.09.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
CNN ; Cardiac Disease Indicators ; Left Ventricle ; MRI ; Volume Estimation

Sažetak
The left ventricle (LV) volume is a major indicator of heart disease.Current clinical procedure for volume estimation from magnetic resonance (MR) images include manual inspection by the experienced cardiologists, which is often a time-consuming process.This paper presents an automated method for LV volume estimation at two points in the time. The method consists of training a convolutional neural network (CNN) with the MR images from the Sunnybrook dataset. After that, the trained model is used to predict left ventricular segments in MR images from the Kaggle dataset. From predicted segments, the ventricular volumes at end-systole (ESV) and end-diastole (EDV) are calculated using numerical integration. The obtained results estimate the ESV and EDV with a mean absolute error of 14.688 and 19.222 mL, respectively.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA