Extracting cardiac shapes and motion of the chick embryo heart outflow tract from four-dimensional optical coherence tomography images

Abstract

Abstract. Recent advances in optical coherence tomography (OCT), and the development of image reconstruction algorithms, enabled four-dimensional (4-D) (three-dimensional imaging over time) imaging of the embryonic heart. To further analyze and quantify the dynamics of cardiac beating, segmentation procedures that can extract the shape of the heart and its motion are needed. Most previous studies analyzed cardiac image sequences using manually extracted shapes and measurements. However, this is time consuming and subject to inter-operator variability. Automated or semi-automated analyses of 4-D cardiac OCT images, although very desirable, are also extremely challenging. This work proposes a robust algorithm to semi automatically detect and track cardiac tissue layers from 4-D OCT images of early (tubular) embryonic hearts. Our algorithm uses a two-dimensional (2-D) deformable double-line model (DLM) to detect target cardiac tissues. The detection algorithm uses a maximum-likelihood estimator and was successfully applied to 4-D in vivo OCT images of the heart outflow tract of day three chicken embryos. The extracted shapes captured the dynamics of the chick embryonic heart outflow tract wall, enabling further analysis of cardiac motion.

Blood flow through the embryonic heart outflow tract during cardiac looping in HH13–HH18 chicken embryos

“…Separation between adjacent cross-sectional planes was 7.5 mm. Following our previously described procedures, these B-scan frame images were synchronized and reconstructed to generate four-dimensional images [35], and the lumen segmented for computational analysis [36].…”

Optical coherence tomography provides an ability to assess mechanical property of cardiac wall of developing outflow tract in embryonic heartin vivo

“…1. Based on the image segmentation, 11 the instantaneous positions of the myocardial wall boundary were obtained from the M-mode images as depicted by the curves in Fig. 2.…”

Optical coherence tomography microangiography for monitoring the response of vascular perfusion to external pressure on human skin tissue

“…To measure the strain, a tissue segmentation algorithm developed by our group was used to calculate the thickness of the deformed tissue structure in the nailfold from the OCT crosssectional structural images. 30 In this method, the structure between the tissue surface subject to the pressure and the specific inner layer insensitive relative to the pressure in the nailfold was segmented by semiautomatically tracking the target tissue layers on the OCT image. 30 As this process was applied to all OCT cross sections, a 3-D tissue structure was segmented.…”

“…30 In this method, the structure between the tissue surface subject to the pressure and the specific inner layer insensitive relative to the pressure in the nailfold was segmented by semiautomatically tracking the target tissue layers on the OCT image. 30 As this process was applied to all OCT cross sections, a 3-D tissue structure was segmented. Thickness of the segmented structure was calculated and thus a 2-D thickness map was created.…”