Magnetic Resonance Imaging (MRI) is a standard-of-care imaging modality for cardiac function assessment and guidance of cardiac interventions thanks to its high image quality and lack of exposure to ionizing radiation. Cardiac health parameters such as left ventricular volume, ejection fraction, myocardial mass, thickness, and strain can be assessed by segmenting the heart from cardiac MRI images. Furthermore, the segmented pre-operative anatomical heart models can be used to precisely identify regions of interest to be treated during minimally invasive therapy. Hence, the use of accurate and computationally efficient segmentation techniques is critical, especially for intra-procedural guidance applications that rely on the peri-operative segmentation of subject-specific datasets without delaying the procedure workflow. Atlas-based segmentation incorporates prior knowledge of the anatomy of interest from expertly annotated image datasets. Typically, the ground truth atlas label is propagated to a test image using a combination of global and local registration. The high computational cost of non-rigid registration motivated us to obtain an initial segmentation using global transformations based on an atlas of the left ventricle from a population of patient MRI images and refine it using well developed technique based on graph cuts. Here we quantitatively compare the segmentations obtained from the global and global plus local atlases and refined using graph cut-based techniques with the expert segmentations according to several similarity metrics, including Dice correlation coefficient, Jaccard coefficient, Hausdorff distance, and Mean absolute distance error.
Region of interest detection is a precursor to many medical image processing and analysis applications, including segmentation, registration and other image manipulation techniques. The optimal region of interest is often selected manually, based on empirical knowledge and features of the image dataset. However, if inconsistently identified, the selected region of interest may greatly affect the subsequent image analysis or interpretation steps, in turn leading to incomplete assessment during computer-aided diagnosis or incomplete visualization or identification of the surgical targets, if employed in the context of pre-procedural planning or image-guided interventions. Therefore, the need for robust, accurate and computationally efficient region of interest localization techniques is prevalent in many modern computer-assisted diagnosis and therapy applications. Here we propose a fully automated, robust, a priori learning-based approach that provides reliable estimates of the left and right ventricle features from cine cardiac MR images. The proposed approach leverages the temporal frame-to-frame motion extracted across a range of short axis left ventricle slice images with small training set generated from les than 10% of the population. This approach is based on histogram of oriented gradients features weighted by local intensities to first identify an initial region of interest depicting the left and right ventricles that exhibits the greatest extent of cardiac motion. This region is correlated with the homologous region that belongs to the training dataset that best matches the test image using feature vector correlation techniques. Lastly, the optimal left ventricle region of interest of the test image is identified based on the correlation of known ground truth segmentations associated with the training dataset deemed closest to the test image. The proposed approach was tested on a population of 100 patient datasets and was validated against the ground truth region of interest of the test images manually annotated by experts. This tool successfully identified a mask around the LV and RV and furthermore the minimal region of interest around the LV that fully enclosed the left ventricle from all testing datasets, yielding a 98% overlap with their corresponding ground truth. The achieved mean absolute distance error between the two contours that normalized by the radius of the ground truth is 0.20 ± 0.09.
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