VesselBoost: A Python Toolbox for Small Blood Vessel Segmentation in Human Magnetic Resonance Angiography Data.

Autor: Xu M; School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia., Ribeiro FL; School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia., Barth M; School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia., Bernier M; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.; Department of Radiology, Harvard Medical School, Boston, MA, USA., Bollmann S; School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia.; Queensland Digital Health Centre, The University of Queensland, Brisbane, QLD, Australia., Chatterjee S; Department of Biomedical Magnetic Resonance, Institute of Experimental Physics, Otto-von-Guericke-University, Magdeburg, ST, Germany.; Data and Knowledge Engineering Group, Faculty of Computer Science, Otto von Guericke University Magdeburg, ST, Germany.; Genomics Research Centre, Human Technopole, Milan, LOM, Italy., Cognolato F; Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia.; ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, QLD, Australia., Gulban OF; Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, LI, Netherlands.; Brain Innovation, Maastricht, LI, Netherlands., Itkyal V; Department of Biotechnology, Indian Institute of Technology, Madras, TN, India., Liu S; School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia.; Australian eHealth Research Centre, CSIRO, Herston, QLD, Australia., Mattern H; Department of Biomedical Magnetic Resonance, Institute of Experimental Physics, Otto-von-Guericke-University, Magdeburg, ST, Germany.; German Center for Neurodegenerative Diseases, Magdeburg, ST, Germany.; Center for Behavioral Brain Sciences, Magdeburg, ST, Germany., Polimeni JR; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.; Department of Radiology, Harvard Medical School, Boston, MA, USA.; Harvard-MIT Program Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA., Shaw TB; School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia., Speck O; Department of Biomedical Magnetic Resonance, Institute of Experimental Physics, Otto-von-Guericke-University, Magdeburg, ST, Germany.; German Center for Neurodegenerative Diseases, Magdeburg, ST, Germany.; Center for Behavioral Brain Sciences, Magdeburg, ST, Germany., Bollmann S; School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia.
Jazyk: angličtina
Zdroj: BioRxiv : the preprint server for biology [bioRxiv] 2024 May 22. Date of Electronic Publication: 2024 May 22.
DOI: 10.1101/2024.05.22.595251
Abstrakt: Magnetic resonance angiography (MRA) performed at ultra-high magnetic field provides a unique opportunity to study the arteries of the living human brain at the mesoscopic level. From this, we can gain new insights into the brain's blood supply and vascular disease affecting small vessels. However, for quantitative characterization and precise representation of human angioarchitecture to, for example, inform blood-flow simulations, detailed segmentations of the smallest vessels are required. Given the success of deep learning-based methods in many segmentation tasks, we here explore their application to high-resolution MRA data, and address the difficulty of obtaining large data sets of correctly and comprehensively labelled data. We introduce VesselBoost , a vessel segmentation package, which utilizes deep learning and imperfect training labels for accurate vasculature segmentation. Combined with an innovative data augmentation technique, which leverages the resemblance of vascular structures, VesselBoost enables detailed vascular segmentations.
Databáze: MEDLINE