  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.
