Browsing by Subject "image analysis"

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    Design and Implementation of Anatomically Inspired Mesenteric and Intestinal Vascular Patterns for Personalized 3D Bioprinting
    (MDPI, 2022-04-27) Cadle, Rachel; Rogozea, Dan; Moldovan, Leni; Moldovan, Nicanor I.; Surgery, School of Medicine
    Recent progress in bioprinting has made possible the creation of complex 3D intestinal constructs, including vascularized villi. However, for their integration into functional units useful for experimentation or implantation, the next challenge is to endow them with a larger-scale, anatomically realistic vasculature. In general, the perfusion of bioprinted constructs has remained difficult, and the current solution is to provide them with mostly linear and simply branched channels. To address this limitation, here we demonstrated an image analysis-based workflow leading through computer-assisted design from anatomic images of rodent mesentery and colon to the actual printing of such patterns with paste and hydrogel bioinks. Moreover, we reverse-engineered the 2D intestinal image-derived designs into cylindrical objects, and 3D-printed them in a support hydrogel. These results open the path towards generation of more realistically vascularized tissue constructs for a variety of personalized medicine applications.
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    DINAVID: A Distributed and Networked Image Analysis System for Volumetric Image Data
    (Cold Spring Harbor Laboratory, 2022-05-11) Han, Shuo; Chen , Alain; Lee, Soonam; Fu, Chichen; Yang, Changye; Wu, Liming; Winfree, Seth; El-Achkar, Tarek M.; Dunn, Kenneth W.; Salama, Paul; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
    Background: The advancement of high content optical microscopy has enabled the acquisition of very large 3D image datasets. Image analysis tools and three dimensional visualization are critical for analyzing and interpreting 3D image volumes. The analysis of these volumes require more computational resources than a biologist may have access to in typical desktop or laptop computers. This is especially true if machine learning tools are being used for image analysis. With the increased amount of data analysis and computational complexity, there is a need for a more accessible, easy-to-use, and efficient network-based/cloud-based 3D image processing system. Results: The Distributed and Networked Analysis of Volumetric Image Data (DINAVID) system was developed to enable remote analysis of 3D microscopy images for biologists. DINAVID is a server/cloud-based system with a simple web interface that allows biologists to upload 3D volumes for analysis and visualization. DINAVID is designed using open source tools and has two main sub-systems, a computational system for 3D microscopy image processing and analysis as well as a 3D visualization system. Conclusions: In this paper, we will present an overview of the DINAVID system and compare it to other tools currently available for microscopy image analysis.