Accurate segmentation of the left ventricle (LV) blood-pool and myocardium is required to compute cardiac function assessment parameters or generate personalized cardiac models for pre-operative planning of minimally invasive therapy. Cardiac Cine Magnetic Resonance Imaging (MRI) is the preferred modality for high resolution cardiac imaging thanks to its capability of imaging the heart throughout the cardiac cycle, while providing tissue contrast superior to other imaging modalities without ionizing radiation. However, there exists an inevitable misalignment between the slices in cine MRI due to the 2D + time acquisition, rendering 3D segmentation methods ineffective. A large part of published work on cardiac MR image segmentation focuses on 2D segmentation methods that yield good results in mid-slices, however with less accurate results for the apical and basal slices. Here, we propose an algorithm to correct for the slice misalignment using a Convolutional Neural Network (CNN)-based regression method, and then perform a 3D graph-cut based segmentation of the LV using atlas shape prior. Our algorithm is able to reduce the median slice misalignment error from 3.13 to 2.07 pixels, and obtain the blood-pool segmentation with an accuracy characterized by a 0.904 mean dice overlap and 0.56 mm mean surface distance with respect to the gold-standard blood-pool segmentation for 9 test cine MR datasets.
Shusil Dangi, Cristian A. Linte, and Ziv Yaniv, "Cine cardiac MRI slice misalignment correction towards full 3D left ventricle segmentation," Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 1057607 (Presented at SPIE Medical Imaging: February 13, 2018; Published: 12 March 2018); https://doi.org/10.1117/12.2294936.
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