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Multi-perspective label based deep learning framework for cerebral vasculature segmentation in whole-brain
Yuxin Li1, Tong Ren1, Junhuai Li1
1Shaanxi Key Laboratory of Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048, China.
Biomedical Optics Express
|July 5, 2022
Summary
This study introduces a novel deep learning framework for segmenting whole mammalian brain vasculature. The method uses multi-perspective labels to accurately map cerebrovascular networks, improving disease research.
Area of Science:
- Neuroscience
- Medical Imaging
- Computer Vision
Background:
- Accurate segmentation of mammalian brain vasculature is crucial for understanding cerebrovascular structure and disease pathogenesis.
- Current deep learning methods struggle with whole-brain fluorescence images due to variations in vessel shape, density, and brightness, leading to inaccurate segmentation.
- Existing methods often use a single label type, failing to capture the complexity of cerebrovascular networks.
Purpose of the Study:
- To develop an advanced deep learning framework for precise segmentation of whole mammalian brain vasculature using multi-perspective labels.
- To overcome the limitations of single-label training in existing methods for complex, high-resolution vascular imaging.
- To improve the accuracy and robustness of cerebrovascular segmentation for large-scale brain datasets.
Main Methods:
- Proposed a deep learning framework utilizing multi-perspective labels derived from binary annotated labels.
- Generated two distinct labels: one for thick vessel central regions and another for vessel skeletons, using morphological operations.
- Designed a three-stage 3D convolutional neural network with specialized sub-networks for thick-vessel enhancement, skeleton enhancement, and multi-channel fusion segmentation.
Main Results:
- The proposed method demonstrated superior performance compared to state-of-the-art techniques on two mouse cerebral vascular datasets from different imaging modalities.
- The multi-perspective label approach and three-stage network architecture enabled more accurate and comprehensive segmentation of cerebrovascular networks.
- Successfully segmented vasculature on large-scale volumes, indicating scalability and robustness.
Conclusions:
- The developed deep learning framework effectively segments whole mammalian brain vasculature with high accuracy, outperforming existing methods.
- The use of multi-perspective labels and a staged convolutional neural network architecture addresses the challenges of imaging variability.
- This method offers a significant advancement for analyzing cerebrovascular structure and aiding in the study of brain diseases.
