Abstract #1054

A method for automatic 3D vasculature segmentation in ex vivo MRI using synthetic data

Chiara Mauri^1,2, Etienne Chollet¹, Adam Willis¹, Aliyah Jama¹, Alisha Mahmood¹, Angelina Ream¹, Itzel Garcia¹, Malak Benlahcen¹, Sariya Wood¹, Stephanie Lin¹, Priyanka Onta¹, Nam Tran¹, Xiangrui Zeng^1,2, Caroline Magnain^1,2, Rogeny Herisse¹, Erendira Xenia Garcia Pallares¹, Malte Hoffmann*^1,2, Bruce Fischl*^1,2,3, and Yaël Balbastre*^1,4

¹Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, ²Harvard Medical School, Department of Radiology, Boston, MA, United States, ³Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁴University College London, London, United Kingdom

Synopsis

Keywords: Blood Vessels, Segmentation

Motivation: Cerebral vascular anatomy is involved in several diseases, but only the largest vessels can be routinely imaged in vivo. Ex vivo MRI offers an alternative at higher resolution (~100 μm) where more vessels are visible. However, it has poor signal-to-noise ratio and nonspecific contrast, and obtaining accurate vessels manual annotations is extremely difficult.

Goal(s): Our goal was to develop a method for automatic 3D vessel segmentation in ex vivo MRI.

Approach: To overcome the lack of vessels manual annotations, we trained a neural network on synthetic data.

Results: The segmentation method tested on real ex vivo MRI images achieved human-level performance.

Impact: Our method for 3D vessel segmentation in ex vivo MRI can be used to build a whole-brain vascular atlas, and study inter-subject variability. It can also be adapted to microscopy and neuropathology, and to other tubular structures (axons and fascicles).

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