Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images

TL;DR

This paper introduces a multiscale template matching and elliptical active disc method for automated segmentation of the left ventricle in cardiac MRI scans, achieving high accuracy with traditional image processing techniques.

Contribution

It presents a novel combination of multiscale template matching and elliptical active disc for efficient LV segmentation in MRI, comparable to deep learning methods.

Findings

Localization success rate of 89.63%

Dice coefficient of 0.873 on diastole slices

Dice coefficient of 0.770 on systole slices

Abstract

Segmentation of the left ventricle in cardiac magnetic resonance imaging MRI scans enables cardiologists to calculate the volume of the left ventricle and subsequently its ejection fraction. The ejection fraction is a measurement that expresses the percentage of blood leaving the heart with each contraction. Cardiologists often use ejection fraction to determine one's cardiac function. We propose multiscale template matching technique for detection and an elliptical active disc for automated segmentation of the left ventricle in MR images. The elliptical active disc optimizes the local energy function with respect to its five free parameters which define the disc. Gradient descent is used to minimize the energy function along with Green's theorem to optimize the computation expenses. We report validations on 320 scans containing 5,273 annotated slices which are publicly available through the Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Segmentation (M&Ms) Challenge. We achieved successful localization of the left ventricle in 89.63% of the cases and a Dice coefficient of 0.873 on diastole slices and 0.770 on systole slices. The proposed technique is based on traditional image processing techniques with a performance on par with the deep learning techniques.