# Estimation of the volume of the left ventricle from MRI images using deep neural networks

## TL;DR

This paper presents a neural network-based system for estimating the volume of the left ventricle from MRI images, combining segmentation and volume estimation techniques, and achieving competitive results in a Kaggle competition.

## Contribution

It introduces an end-to-end deep learning approach with a novel training strategy using multiple datasets and a variance estimation method for improved LV volume prediction.

## Key findings

- Ranked 4th in the Kaggle competition
- Effective integration of segmentation results for volume estimation
- Demonstrated the utility of multi-dataset training with different labels

## Abstract

Segmenting human left ventricle (LV) in magnetic resonance imaging (MRI) images and calculating its volume are important for diagnosing cardiac diseases. In 2016, Kaggle organized a competition to estimate the volume of LV from MRI images. The dataset consisted of a large number of cases, but only provided systole and diastole volumes as labels. We designed a system based on neural networks to solve this problem. It began with a detector combined with a neural network classifier for detecting regions of interest (ROIs) containing LV chambers. Then a deep neural network named hypercolumns fully convolutional network was used to segment LV in ROIs. The 2D segmentation results were integrated across different images to estimate the volume. With ground-truth volume labels, this model was trained end-to-end. To improve the result, an additional dataset with only segmentation label was used. The model was trained alternately on these two datasets with different types of teaching signals. We also proposed a variance estimation method for the final prediction. Our algorithm ranked the 4th on the test set in this competition.