Gabriel Maher1, David Parker2, Nathan Wilson3, Alison Marsden4. 1. Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA. 2. Research Computing, Stanford University, Stanford, CA, USA. 3. Open Source Medical Software Corporation, Los Angeles, CA, USA. 4. Pediatric Cardiology, Bioengineering, Stanford University, Stanford, CA, USA. amarsden@stanford.edu.
Abstract
PURPOSE: We accelerate a pathline-based cardiovascular model building method by training machine learning models to directly predict vessel lumen surface points from computed tomography (CT) and magnetic resonance (MR) medical image data. METHODS: We formulate vessel lumen detection as a regression task using a polar coordiantes representation. RESULTS: Neural networks trained with our regression formulation allow predictions to be made with significantly higher accuracy than existing methods that identify the vessel lumen through binary pixel classification. The regression formulation enables machine learning models to be trained end-to-end for vessel lumen detection without post-processing steps that reduce accuracy. CONCLUSION: By employing our models in a pathline-based cardiovascular model building pipeline we substantially reduce the manual segmentation effort required to build accurate cardiovascular models, and reduce the overall time required to perform patient-specific cardiovascular simulations. While our method is applied here for cardiovascular model building it is generally applicable to segmentation of tree-like and tubular structures from image data.
PURPOSE: We accelerate a pathline-based cardiovascular model building method by training machine learning models to directly predict vessel lumen surface points from computed tomography (CT) and magnetic resonance (MR) medical image data. METHODS: We formulate vessel lumen detection as a regression task using a polar coordiantes representation. RESULTS: Neural networks trained with our regression formulation allow predictions to be made with significantly higher accuracy than existing methods that identify the vessel lumen through binary pixel classification. The regression formulation enables machine learning models to be trained end-to-end for vessel lumen detection without post-processing steps that reduce accuracy. CONCLUSION: By employing our models in a pathline-based cardiovascular model building pipeline we substantially reduce the manual segmentation effort required to build accurate cardiovascular models, and reduce the overall time required to perform patient-specific cardiovascular simulations. While our method is applied here for cardiovascular model building it is generally applicable to segmentation of tree-like and tubular structures from image data.
Authors: Gabriel D Maher; Casey M Fleeter; Daniele E Schiavazzi; Alison L Marsden Journal: Comput Methods Appl Mech Eng Date: 2021-08-14 Impact factor: 6.588
Authors: Matthew H Park; Yuanjia Zhu; Annabel M Imbrie-Moore; Hanjay Wang; Mateo Marin-Cuartas; Michael J Paulsen; Y Joseph Woo Journal: Front Cardiovasc Med Date: 2021-07-08