  Assessment of cardiovascular disease (CVD) with cine magnetic resonance
imaging (MRI) has been used to non-invasively evaluate detailed cardiac
structure and function. Accurate segmentation of cardiac structures from cine
MRI is a crucial step for early diagnosis and prognosis of CVD, and has been
greatly improved with convolutional neural networks (CNN). There, however, are
a number of limitations identified in CNN models, such as limited
interpretability and high complexity, thus limiting their use in clinical
practice. In this work, to address the limitations, we propose a lightweight
and interpretable machine learning model, successive subspace learning with the
subspace approximation with adjusted bias (Saab) transform, for accurate and
efficient segmentation from cine MRI. Specifically, our segmentation framework
is comprised of the following steps: (1) sequential expansion of near-to-far
neighborhood at different resolutions; (2) channel-wise subspace approximation
using the Saab transform for unsupervised dimension reduction; (3) class-wise
entropy guided feature selection for supervised dimension reduction; (4)
concatenation of features and pixel-wise classification with gradient boost;
and (5) conditional random field for post-processing. Experimental results on
the ACDC 2017 segmentation database, showed that our framework performed better
than state-of-the-art U-Net models with 200$\times$ fewer parameters in
delineating the left ventricle, right ventricle, and myocardium, thus showing
its potential to be used in clinical practice.
