ArticleConference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops2020
A topological encoding convolutional neural network for segmentation of 3D multiphoton images of brain vasculature using persistent homology.
Article in Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Interpretable CRAM‑Enhanced Lightweight Dual‑Branch CNN for Real‑Time Breast Cancer Histopathology in Internet‑of‑Medical‑Things Environments.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Topological Neuroscience: Linking Circuits to Function.Annual review of neuroscience · 2025Review
- Unsupervised Segmentation of 3D Microvascular Photoacoustic Images Using Deep Generative Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI.Statistical atlases and computational models of the heart. STACOM (Workshop) · 2020Article
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Authors and funding
6 authors.
Funding
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 out-performs 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.
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Registered trials
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