The Deep Poincar\'e Map: A Novel Approach for Left Ventricle Segmentation

TL;DR

This paper introduces a novel deep learning-based iterative segmentation method for the left ventricle in cardiac MRI images, utilizing a Poincaré map-guided policy to improve accuracy despite limited data and artifacts.

Contribution

It proposes a new dynamic-based labeling scheme combined with a policy model guiding an agent for LV segmentation, outperforming previous methods on multiple datasets.

Findings

Outperforms previous methods on SCD and STACOM datasets

Demonstrates good transferability across datasets

Uses Poincaré map magnitude difference as a stopping criterion

Abstract

Precise segmentation of the left ventricle (LV) within cardiac MRI images is a prerequisite for the quantitative measurement of heart function. However, this task is challenging due to the limited availability of labeled data and motion artifacts from cardiac imaging. In this work, we present an iterative segmentation algorithm for LV delineation. By coupling deep learning with a novel dynamic-based labeling scheme, we present a new methodology where a policy model is learned to guide an agent to travel over the the image, tracing out a boundary of the ROI -- using the magnitude difference of the Poincar\'e map as a stopping criterion. Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge. Our method outperforms the previous research over many metrics. In order to demonstrate the transferability of our method we present encouraging results over the STACOM 2011 data, when using a model trained on the SCD dataset.