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arXiv 2109.01132eess.IVcs.CV

A New Semi-Automated Algorithm for Volumetric Segmentation of the Left Ventricle in Temporal 3D Echocardiography Sequences

University of Alberta(阿尔伯塔大学);Mazankowski Alberta Heart Institute(马赞科夫斯基阿尔伯塔心脏研究所)

Deepa Krishnaswamy, Abhilash R. Hareendranathan, Tan Suwatanaviroj, Pierre Boulanger, Harald Becher, Michelle Noga, Kumaradevan Punithakumar

英文摘要:

Purpose: Echocardiography is commonly used as a non-invasive imaging tool in clinical practice for the assessment of cardiac function. However, delineation of the left ventricle is challenging due to the inherent properties of ultrasound imaging, such as the presence of speckle noise and the low signal-to-noise ratio. Methods: We propose a semi-automated segmentation algorithm for the delineation of the left ventricle in temporal 3D echocardiography sequences. The method requires minimal user interaction and relies on a diffeomorphic registration approach. Advantages of the method include no dependence on prior geometrical information, training data, or registration from an atlas. Results: The method was evaluated using three-dimensional ultrasound scan sequences from 18 patients from the Mazankowski Alberta Heart Institute, Edmonton, Canada, and compared to manual delineations provided by an expert cardiologist and four other registration algorithms. The segmentation approach yielded the following results over the cardiac cycle: a mean absolute difference of 1.01 (0.21) mm, a Hausdorff distance of 4.41 (1.43) mm, and a Dice overlap score of 0.93 (0.02). Conclusions: The method performed well compared to the four other registration algorithms.

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