Abstract
We report a generic method for automatic segmentation of endoscopic optical coherence tomography (OCT) images. In this method, OCT images are first processed with L1 -L0 norm minimization based de-noising and smoothing algorithms to increase the signal-to-noise ratio (SNR) and enhance the contrast between adjacent layers. The smoothed images are then formulated into cost graphs based on their vertical gradients. After that, tissue-layer segmentation is performed with the shortest path search algorithm. The efficacy and capability of this method are demonstrated by automatically and robustly identifying all five interested layers of guinea pig esophagus from in vivo endoscopic OCT images. Furthermore, thanks to the ultrahigh resolution, high SNR of endoscopic OCT images and the high segmentation accuracy, this method permits in vivo optical staining histology and facilitates quantitative analysis of tissue geometric properties, which can be very useful for studying tissue pathologies and potentially aiding clinical diagnosis in real time.
Highlights
Optical coherence tomography (OCT) is a powerful imaging technology for assessing biological tissue morphology, blood flow, optical and mechanical properties [1,2,3,4]
One example of direct clinical relevance is the thickness of retinal layer and nerve fiber layer based on OCT images for glaucoma staging [8, 9]
These approaches, are intrinsically sensitive to noise and could potentially fail to detect tissue layer boundaries correctly if the boundaries seem to be discontinued in the images due to view blocking, tissue folding, or low contrast resulting from low signal-to-noise ratio (SNR)
Summary
Optical coherence tomography (OCT) is a powerful imaging technology for assessing biological tissue morphology, blood flow, optical and mechanical properties [1,2,3,4]. The intensity variation-based method has been successfully applied to segment retinal layers from 3D OCT data sets [15] These approaches, are intrinsically sensitive to noise and could potentially fail to detect tissue layer boundaries correctly if the boundaries seem to be discontinued in the images due to view blocking, tissue folding, or low contrast resulting from low signal-to-noise ratio (SNR). Endoscopic OCT images come with their own challenges, such as steep layer boundary slopes due to tissue folding, view blocking by mucus or some debris (such as food debris in esophagus), and image distortion caused by nonuniform azimuthal scanning speed To address these challenges, we propose an automatic and robust layer segmentation approach for endoscopic OCT images.
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