TY - GEN
T1 - Topological Analysis of Mouse Brain Vasculature via 3d Light-Sheet Microscopy Images
AU - Yao, Jiachen
AU - Hagemann, Nina
AU - Xiong, Qiaojie
AU - Chen, Jianxu
AU - Hermann, Dirk M.
AU - Chen, Chao
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Vascular networks play a crucial role in understanding brain functionalities. Brain integrity and function, neuronal activity and plasticity, which are crucial for learning, are actively modulated by their local environments, specifically vascular networks. With recent developments in high-resolution 3D light-sheet microscopy imaging together with tissue processing techniques, it becomes feasible to obtain and examine large-scale brain vasculature in mice. To establish a structural foundation for functional study, however, we need advanced image analysis and structural modeling methods.Existing works use geometric features such as thickness, tortuosity, etc. However, geometric features cannot fully capture structural characteristics such as the richness of branches, connectivity, etc. In this paper, we study the morphology of brain vasculature through a topological lens. We extract topological features 1 based on the theory of topological data analysis. Comparing of these robust and multi-scale topological structural features across different brain anatomical structures and between normal and obese populations sheds light on their promising future in studying neurological diseases.
AB - Vascular networks play a crucial role in understanding brain functionalities. Brain integrity and function, neuronal activity and plasticity, which are crucial for learning, are actively modulated by their local environments, specifically vascular networks. With recent developments in high-resolution 3D light-sheet microscopy imaging together with tissue processing techniques, it becomes feasible to obtain and examine large-scale brain vasculature in mice. To establish a structural foundation for functional study, however, we need advanced image analysis and structural modeling methods.Existing works use geometric features such as thickness, tortuosity, etc. However, geometric features cannot fully capture structural characteristics such as the richness of branches, connectivity, etc. In this paper, we study the morphology of brain vasculature through a topological lens. We extract topological features 1 based on the theory of topological data analysis. Comparing of these robust and multi-scale topological structural features across different brain anatomical structures and between normal and obese populations sheds light on their promising future in studying neurological diseases.
KW - Persistent Homology
KW - Topological Data Analysis
KW - Vasculature
UR - https://www.scopus.com/pages/publications/85203393917
U2 - 10.1109/ISBI56570.2024.10635226
DO - 10.1109/ISBI56570.2024.10635226
M3 - Conference contribution
AN - SCOPUS:85203393917
T3 - Proceedings - International Symposium on Biomedical Imaging
BT - IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings
PB - IEEE Computer Society
T2 - 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024
Y2 - 27 May 2024 through 30 May 2024
ER -