Abstract
The segmentation of the heart is usually demanded in the clinical practice for computing functional parameters in patients, such as ejection fraction, cardiac output, peak ejection rate, or filling rate. Because of the time required, the manual delineation is typically limited to the left ventricle at the end-diastolic and end-systolic phases, which is insufficient for computing some of these parameters (e.g., peak ejection rate or filling rate). Common computer-aided (semi-)automated approaches for the segmentation task are computationally demanding, and an initialization step is frequently needed. This work is intended to address the aforementioned problems by providing an image-driven method for the accurate segmentation of the heart from computed tomography scans. The resulting algorithm is fast and fully automatic (even the region of interest is delimited without human intervention). The proposed methodology relies on image processing and analysis techniques (such as multi-thresholding based on statistical local and global parameters, mathematical morphology, and image filtering) and also on prior knowledge about the cardiac structures involved. Segmentation results are validated through the comparison with manually delineated ground truth, both qualitatively (no noticeable errors found after visual inspection) and quantitatively (mass overlapping over 90%).
Highlights
Cardiovascular disease is the leading direct or contributing cause of non-accidental deaths in the world [1]
An example of this effort is the delineation of the left ventricle (LV) of the heart, which turns out to be an important tool in the assessment of cardiac functional parameters such as ejection fraction, myocardium mass, or stroke volume
3 Results Following the methodology described above, the segmentation algorithm introduced in this paper was applied to 32 clinical exams from randomly selected adult patients
Summary
Cardiovascular disease is the leading direct or contributing cause of non-accidental deaths in the world [1]. The P slices of the scan are thresholded with their corresponding values μsup(k), allowing us to isolate bones and tissues where oxygenated blood flows from the rest of the image At this point, the objects of the resulting binary mask are labeled, and the spine is selected as the object which contains the pixels obtained by the process described in Subsection 2.1.2. This feature allows us to precisely segment the left heart by means of the algorithm Isodata [33], which provides an optimal result with a low Figure 10 Computation of the final mask.
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