Abstract #0956

VesselDigitizer: A deep learning-powered 3D Slicer extension for digitizing cerebral vasculature from 3D medical images

Zhensen Chen^1,2, Lixin Liu¹, Xiaoqian Chao¹, Jian Wang³, Yujun Liao^4,5, Xuesong Li⁶, and He Wang^1,2

¹Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China, ²MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China, ³Department of Neurosurgery, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou Medical Center, Changzhou, China, ⁴Department of Neurosurgery, Fudan University Huashan Hospital, Shanghai, China, ⁵Clinical Medical Center of Neurosurgery, Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China, ⁶School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

Synopsis

Keywords: Software Tools, Vessels

Motivation: Accurate and fast digitization of the cerebral vasculature can facilitate research on cerebrovascular health, the development of novel imaging biomarkers, and clinical applications.

Goal(s): To develop a user-friendly tool with high flexibility and extensibility to segment, label, and quantify cerebral vasculature by taking advantage of deep learning.

Approach: The tool VesselDigitizer was developed as an extension of 3D Slicer to leverage its image processing and visualization capability. Deep-learning powered modules for segmentation, refinement, and labeling of the vasculature were developed.

Results: VesselDigitizer enables fast segmentation and vessel labeling and provides various handy tools to identify and correct incorrect segmentation and labeling.

Impact: VesselDigitizer makes large-scale analysis of cerebral vasculatures easier and more accurate.

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