Ramakrishnan, Vignesh and Schoenmehl, Rebecca and Artinger, Annalena and Winter, Lina and Boeck, Hendrik and Schreml, Stephan and Guertler, Florian and Daza, Jimmy and Schmitt, Volker H. and Mamilos, Andreas and Arbelaez, Pablo and Teufel, Andreas and Niedermair, Tanja and Topolcan, Ondrej and Karlikova, Marie and Sossalla, Samuel and Wiedenroth, Christoph B. and Rupp, Markus and Brochhausen, Christoph (2023) 3D Visualization, Skeletonization and Branching Analysis of Blood Vessels in Angiogenesis. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 24 (9): 7714. ISSN 1661-6596, 1422-0067
Full text not available from this repository. (Request a copy)Abstract
Angiogenesis is the process of new blood vessels growing from existing vasculature. Visualizing them as a three-dimensional (3D) model is a challenging, yet relevant, task as it would be of great help to researchers, pathologists, and medical doctors. A branching analysis on the 3D model would further facilitate research and diagnostic purposes. In this paper, a pipeline of vision algorithms is elaborated to visualize and analyze blood vessels in 3D from formalin-fixed paraffin-embedded (FFPE) granulation tissue sections with two different staining methods. First, a U-net neural network is used to segment blood vessels from the tissues. Second, image registration is used to align the consecutive images. Coarse registration using an image-intensity optimization technique, followed by finetuning using a neural network based on Spatial Transformers, results in an excellent alignment of images. Lastly, the corresponding segmented masks depicting the blood vessels are aligned and interpolated using the results of the image registration, resulting in a visualized 3D model. Additionally, a skeletonization algorithm is used to analyze the branching characteristics of the 3D vascular model. In summary, computer vision and deep learning is used to reconstruct, visualize and analyze a 3D vascular model from a set of parallel tissue samples. Our technique opens innovative perspectives in the pathophysiological understanding of vascular morphogenesis under different pathophysiological conditions and its potential diagnostic role.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | IMAGE REGISTRATION; RECONSTRUCTION; SEGMENTATION; angiogenesis; 3D visualization; neural networks; image registration and segmentation; artificial intelligence; digital pathology; biobanking |
| Subjects: | 500 Science > 570 Life sciences 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Unfallchirurgie Medicine > Lehrstuhl für Dermatologie und Venerologie Medicine > Lehrstuhl für Innere Medizin II Medicine > Lehrstuhl für Pathologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 10 Mar 2024 13:22 |
| Last Modified: | 10 Mar 2024 13:23 |
| URI: | https://pred.uni-regensburg.de/id/eprint/59574 |
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