Abstract
In this study, we present several image segmentation techniques for various image scales and modalities. We consider cellular-, organ-, and whole organism-levels of biological structures in cardiovascular applications. Several automatic segmentation techniques are presented and discussed in this work. The overall pipeline for reconstruction of biological structures consists of the following steps: image pre-processing, feature detection, initial mask generation, mask processing, and segmentation post-processing. Several examples of image segmentation are presented, including patient-specific abdominal tissues segmentation, vascular network identification and myocyte lipid droplet micro-structure reconstruction.
Highlights
Numerical modeling in biomedical applications has remained a challenge for many years.Individualized numerical simulations of physiological processes in the human body received a great deal of attention over several decades, and a vast number of models have been described in the literature
We focus on two cardiovascular applications: haemodynamics and electrophysiology modeling
We present in detail the segmentation techniques for cardiovascular biomedical applications, the enhanced automatic algorithm for coronary and cerebral arteries segmentation with alternative segmentation correction step, and the reconstruction of inhomogeneous structure of lipid droplets in human myocyte cells
Summary
Numerical modeling in biomedical applications has remained a challenge for many years. Individualized numerical simulations of physiological processes in the human body received a great deal of attention over several decades, and a vast number of models have been described in the literature. Contemporary resolution of medical images and new algorithms for their post-processing allow us to develop high resolution numerical models of various processes at cellular-, organ-, and whole organism-levels [1,2,3,4,5]. We present and develop methods and algorithms for construction of patient-specific and cellular micro-structure discrete geometric models for cardiovascular biomedical applications. Each biomedical application imposes specific restrictions on both the input medical images and the output patient-specific model, and, calls for a specific class of 3D segmentation methods. Various medical image segmentation techniques have been developed [6,7,8]
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