Successive Subspace Learning for Cardiac Disease Classification with Two-phase Deformation Fields from Cine MRI

TL;DR

This paper introduces a lightweight, interpretable successive subspace learning framework utilizing two-phase deformation fields from cine MRI for accurate cardiac disease classification, especially effective with small training datasets.

Contribution

The study presents a novel hierarchical SSL model combined with deformation fields, offering improved interpretability and performance over deep learning models in cardiac disease classification.

Findings

Achieves superior classification accuracy compared to 3D CNNs.

Uses 140 times fewer parameters than deep learning models.

Effective with small training samples.

Abstract

Cardiac cine magnetic resonance imaging (MRI) has been used to characterize cardiovascular diseases (CVD), often providing a noninvasive phenotyping tool.~While recently flourished deep learning based approaches using cine MRI yield accurate characterization results, the performance is often degraded by small training samples. In addition, many deep learning models are deemed a ``black box," for which models remain largely elusive in how models yield a prediction and how reliable they are. To alleviate this, this work proposes a lightweight successive subspace learning (SSL) framework for CVD classification, based on an interpretable feedforward design, in conjunction with a cardiac atlas. Specifically, our hierarchical SSL model is based on (i) neighborhood voxel expansion, (ii) unsupervised subspace approximation, (iii) supervised regression, and (iv) multi-level feature integration. In addition, using two-phase 3D deformation fields, including end-diastolic and end-systolic phases, derived between the atlas and individual subjects as input offers objective means of assessing CVD, even with small training samples. We evaluate our framework on the ACDC2017 database, comprising one healthy group and four disease groups. Compared with 3D CNN-based approaches, our framework achieves superior classification performance with 140$\times$ fewer parameters, which supports its potential value in clinical use.