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13 March 2019 Vessel segmentation with deep learning (Conference Presentation)
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Abstract
Vessel segmentation, which is to distinguish blood vessels from the surrounding tissue in images, is a pre-processing step that is often required for the analysis of a vascular network. Three-dimensional segmentation is often challenging in the presence of noise, and a simple thresholding method usually does not work well. Here we have integrated features extracted from 3D images obtained from two-photon in-vivo microscopy with deep learning to do vessel segmentation. The inputs are eigenvalues of the Hessian matrix for each voxel for three different Gaussian filters of width 2, 3, 4 μm and the intensity normalized within the x-y plane. The network is composed of 3-5 layers and each with 3-6 hidden units and is trained for two mouse brain vasculature networks and tested on a third one. The results show a significant improvement compared to a simple thresholding method and are going to be compared with other segmentation methods such as particle filters and enhancement filters. Preliminary results of segmenting OCT data are also obtained.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaojun Cheng, Sreekanth Kura, and David Boas "Vessel segmentation with deep learning (Conference Presentation)", Proc. SPIE 10883, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI, 108830W (13 March 2019); https://doi.org/10.1117/12.2508038
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