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

arXiv: https://arxiv.org/abs/1702.03833 · HTML: https://mlsanity.com/paper/1702.03833/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2017.

## Built on by (most cited first, 5 of 5)

- [A Generalizable Deep Learning System for Cardiac MRI](https://mlsanity.com/paper/2312.00357/) (2023)
- [Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2201.12805/) (2022)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views](https://mlsanity.com/paper/2308.08038/) (2023)
- [DeepSPV: A Deep Learning Pipeline for 3D Spleen Volume Estimation from 2D Ultrasound Images](https://mlsanity.com/paper/2411.11190/) (2024)

## Abstract (arXiv)

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.