Definition of Tubular Anatomic Structures from Arbitrary Stereo Lithographic Surface
Paper in proceedings, 2017

An accurate description of anatomies and dynamics of vessels is crucial to understand their characteristics and improve surgical techniques, thus it is the basis, in addition to surgeon experience, on which stent design and operation procedures rely. The process of producing this description is user intensive, and recent improvement in image processing of medical3D imaging allows for a more automated workflow. However, there is a need to bridge the gap from a processed geometry to a robust mathematical computational grid. By sequentially segmenting a tubular anatomic structure, here defined by a stereo lithographic (STL) surface, an initial centerline is formed by connecting centroids of orthogonal cross-sectional contours along the length of the structure. Relying on the initial centerline, a set of non-overlapping 2D cross sectional contours are defined along the centerline, a centerline which is updated after the 2D contours are produced. After a second iteration of producing 2D contours and updating the centerline, a full description of the structure is created. Our method for describing vessel geometry shows good coherence to existing method. The main advantages of our method include the possibility of having arbitrary triangulated STL surface input, automated centerline definition, safety against intersecting cross-sectional contours and automatic clean-up of local kinks and wrinkles.


surface curvature


vessel geometry



Johan Bondesson


Ga-Young Suh

Department of Surgery, Stanford University

Torbjörn Lundh

University of Gothenburg

Chalmers, Mathematical Sciences

Jason T. Lee

Department of Surgery, Stanford University

Michael D. Dake

Department of Cardiothoracic Surgery, Stanford University

Christopher P. Cheng

Department of Surgery, Stanford University

Engineering Health, Nov. 8, 2017, Chalmers

Subject Categories

Computational Mathematics

Other Medical Biotechnology

Medical Materials

Medical Image Processing

Areas of Advance

Life Science Engineering (2010-2018)

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