# Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization

## TL;DR

This paper introduces a fully automated deep learning and spline optimization method for feature tracking in cardiac MRI, enabling accurate segmentation and strain analysis for cardiac function assessment.

## Contribution

It combines CNN-based segmentation with spline fitting for automated, reproducible cardiac feature tracking in MRI, improving over manual methods.

## Key findings

- Segmentation accuracy ~97% pixel accuracy
- IoU ~87% across classes
- Significant difference in strain between control and HCM patients

## Abstract

Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an area of interest for quantification of regional cardiac function from balanced, steady state free precession (SSFP) cine sequences. However, currently available techniques lack full automation, limiting reproducibility. We propose a fully automated technique whereby a CMR image sequence is first segmented with a deep, fully convolutional neural network (CNN) architecture, and quadratic basis splines are fitted simultaneously across all cardiac frames using least squares optimization. Experiments are performed using data from 42 patients with hypertrophic cardiomyopathy (HCM) and 21 healthy control subjects. In terms of segmentation, we compared state-of-the-art CNN frameworks, U-Net and dilated convolution architectures, with and without temporal context, using cross validation with three folds. Performance relative to expert manual segmentation was similar across all networks: pixel accuracy was ~97%, intersection-over-union (IoU) across all classes was ~87%, and IoU across foreground classes only was ~85%. Endocardial left ventricular circumferential strain calculated from the proposed pipeline was significantly different in control and disease subjects (-25.3% vs -29.1%, p = 0.006), in agreement with the current clinical literature.