EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark
  Detection on Echocardiograms
Masoud Mokhtari Mobina Mahdavi Hooman Vaseli Christina Luong Purang Abolmaesumi Teresa S. M. Tsang Renjie Liao
The functional assessment of the left ventricle chamber of the heart requires
detecting four landmark locations and measuring the internal dimension of the
left ventricle and the approximate mass of the surrounding muscle. The key
challenge of automating this task with machine learning is the sparsity of
clinical labels, i.e., only a few landmark pixels in a high-dimensional image
are annotated, leading many prior works to heavily rely on isotropic label
smoothing. However, such a label smoothing strategy ignores the anatomical
information of the image and induces some bias. To address this challenge, we
introduce an echocardiogram-based, hierarchical graph neural network (GNN) for
left ventricle landmark detection (EchoGLAD). Our main contributions are: 1) a
hierarchical graph representation learning framework for multi-resolution
landmark detection via GNNs; 2) induced hierarchical supervision at different
levels of granularity using a multi-level loss. We evaluate our model on a
public and a private dataset under the in-distribution (ID) and
out-of-distribution (OOD) settings. For the ID setting, we achieve the
state-of-the-art mean absolute errors (MAEs) of 1.46 mm and 1.86 mm on the two
datasets. Our model also shows better OOD generalization than prior works with
a testing MAE of 4.3 mm.