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

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