A topological encoding convolutional neural network for segmentation of 3D multiphoton images of brain vasculature using persistent homology
Name
Haft-Javaherian_A_Topological_Encoding_Convolutional_Neural_Network_for_Segmentation_of_3D_CVPRW_2020_paper.pdf
Description
Accepted version
Size
3.86 MB
Format
Adobe PDF
Checksum (MD5)
b51e6516353dd5e571d1e1777061dff9
Author(s)
Haft-Javaherian, Mohammad
Golland, Polina
Bouma, Brett E.
Date Issued
June 2020
Journal
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Publisher
IEEE
Citation
Haft-Javaherian, Mohammad et al. “A topological encoding convolutional neural network for segmentation of 3D multiphoton images of brain vasculature using persistent homology.” Paper presented at the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle WA, 14-19 June 2020, IEEE © 2020 The Author(s)
Version
Author's final manuscript
Abstract
The clinical evidence suggests that cognitive disorders are associated with vasculature dysfunction and decreased blood flow in the brain. Hence, a functional understanding of the linkage between brain functionality and the vascular network is essential. However, methods to systematically and quantitatively describe and compare structures as complex as brain blood vessels are lacking. 3D imaging modalities such as multiphoton microscopy enables researchers to capture the network of brain vasculature with high spatial resolutions. Nonetheless, image processing and inference are some of the bottlenecks for biomedical research involving imaging, and any advancement in this area impacts many research groups. Here, we propose a topological encoding convolutional neural network based on persistent homology to segment 3D multiphoton images of brain vasculature. We demonstrate that our model outperforms state-of-the-art models in terms of the Dice coefficient and it is comparable in terms of other metrics such as sensitivity. Additionally, the topological characteristics of our model's segmentation results mimic manual ground truth. Our code and model are open source at https://github.com/mhaft/DeepVess.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPRW50498.2020.00503
