Automatic Volumetric Analysis of the Left Ventricle in 3D Apical Echocardiographs
Apically-acquired 3D echocardiographs (echoes) are becoming a standard data component in the clinical evaluation of left ventricular (LV) function. Ejection fraction (EF) is one of the key quantitative biomarkers derived from echoes and used by echocardiographers to study a patient's heart function. In present clinical practice, EF is either grossly estimated by experienced observers, approximated using orthogonal 2D slices and Simpson's method, determined by manual segmentation of the LV lumen, or measured using semi-automatic proprietary software such as Philips QLab-3DQ. Each of these methods requires particular skill by the operator, and may be time-intensive, subject to variability, or both.
To address this, I have developed a novel, fully automatic method to LV segmentation in 3D echoes that offers EF calculation on clinical datasets at the push of a button. The solution is built on a pipeline that utilizes a number of image processing and feature detection methods specifically adopted to the 3D ultrasound modality. It is designed to be reasonably robust at handling dropout and missing features typical in clinical echocardiography. It is hypothesized that this method can displace the need for sonographer input, yet provide results statistically indistinguishable from those of experienced sonographers using QLab-3DQ, the current gold standard that is employed at Duke University Hospital.
A pre-clinical validation set, which was also used for iterative algorithm development, consisted of 70 cases previously seen at Duke. Of these, manual segmentations of 7 clinical cases were compared to the algorithm. The final algorithm predicts EF within ± 0.02 ratio units for 5 of them, and ± 0.09 units for the remaining 2 cases, within common clinical tolerance. Another 13 of the cases, often used for sonographer training and rated as having good image quality, were analyzed using QLab-3DQ, in which 11 cases showed concordance (± 0.10) with the algorithm. The remaining 50 cases retrospectively recruited at Duke and representative of everyday image quality showed 62% concordance (± 0.10) of QLab-3DQ with the algorithm. The fraction of concordant cases is highly dependent on image quality, and concordance improves greatly upon disqualification of poor quality images. Visual comparison of the QLab-3DQ segmentation to my algorithm overlaid on top of the original echoes also suggests that my method may be preferable or of high utility even in cases of EF discordance. This paper describes the algorithm and offers justifications for the adopted methods. The paper also discusses the design of a retrospective clinical trial now underway at Duke with 60 additional unseen cases intended only for independent validation.
Medical imaging and radiology
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